Analyses for stickleback landscape parasitology study
Pascal I. Hablützel and Io S. Deflem
2022-07-09
1. Description
This R Markdown document describes the analyses performed for the manuscript entitled “Environmental pollution correlates with parasite infection across a riverine landscape” by Io S. Deflem, Seppe Marchand, Federico C.F. Calboli, Joost A.M. Raeymaekers, Filip A.M. Volckaert and Pascal I. Hablützel.
The analyses were run in R 4.1.2
2. Study area and sampling
Up to thirty 0+ three-spined sticklebacks were sampled at 37 locations in the Dijle and Demer basins in Belgium during autumn 2016 under a permit of the Flemish Agency Nature and Forest (Fig. 1). Both basins together cover a continuous surface area of 3,624 km² with the furthest two sampling sites being located 117 km apart (distance measured along rivers). All locations included small and relatively slow flowing streams (drop off from highest to lowest point is 18 m) covering a wide range of ecological, hydromorphological, and physico-chemical characteristics. Fish were caught using a dip net.
3. Setting up working environment
# Empty environment
rm(list=ls())
# Set working directory to location where script is stored
setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) # requires installation of package "rstudioapi"4 Loading and preparing host and parasite data
Fish were euthanized with a lethal dose of MS222 on the day of capture, following directions of the KU Leuven Animal Ethics Commission, and stored at -20 °C. Fish were kept in separate containers per site at all times. Lab based parasite screening of thawed fish involved placing individual fish in 5 or 10 ml cryo-tubes with 1 or 2 ml of distilled water. Following a vigorous shake of 10 s, the liquid was poured into a Petri dish and ectoparasites were identified and counted using a stereomicroscope. Fish were rinsed and checked again for the presence of ectoparasites on skin and fins. The intestines were examined for endoparasites. Before dissection, fish weight (± 0.1 mg) and standard length (± 1 mm) were recorded. To quantify body condition, we calculated the scaled mass index (SMI; Maceda-Veiga et al., 2014; Peig & Green, 2009). Sex was determined during dissection by inspection of gonad development. A total of 668 fish were dissected, which amounts to approximately 20 fish per location, with the exception of seven locations where only 10 fish were screened for the presence of macroparasites. Ecto- and endoparasites were morphologically identified to species level whenever possible.
# Parasite data
data <- read.csv("data_2016_2303.csv", sep=';')
data$site <- as.factor(data$site)
# Calculate parasite parameters
names(data)## [1] "site" "fish" "parspeciesrichness"
## [4] "div_shannon" "div_simpson" "temp"
## [7] "pH" "conductivity" "nitrogen"
## [10] "phosphorus" "oxygen" "netcen"
## [13] "updist" "updist2" "updist3"
## [16] "fishspeciesrichness" "weight" "weigh..g."
## [19] "length" "SMI" "Sex"
## [22] "Gyr" "Tri" "Glu"
## [25] "ecto_screener" "Con" "CysL"
## [28] "Pro" "Aca" "Cam"
## [31] "Ang" "CysI" "endo_screener"
## [34] "PI" "PI_ecto" "PI_endo"#parasite data is overdispersed (mostly so for Trichodina), if using average abundance data, species matrix needs to be transformed
datao <- na.omit(data[,c(1,22:24,26:32)]) #remove fish without parasite counts
library(vegan)## Loading required package: permute## Loading required package: lattice## This is vegan 2.6-4ddata <- dispweight(datao[,-1]) #correct for overdispersion of the parasite count data
avab <- aggregate(ddata, by = list(datao[,1]), function(x){mean(x, na.rm =T)})
prev = aggregate(data[,c(22:24,26:32)], by = list(data[,1]), function(x){sum(x >0, na.rm = T)/length(x)})
medin = aggregate(data[,c(22:24,26:32)], by = list(data[,1]), function(x){median(x[x >0], na.rm = T)})
pa = aggregate(data[,c(22:24,26:32)], by = list(data[,1]), function(x){ifelse(mean(x, na.rm =T)>0,1,0)})
avab[is.na(avab)] <- 0
prev[is.na(prev)] <- 0
medin[is.na(medin)] <- 0
# Host condition data
avcondition <- aggregate(data$SMI, by = list(data[,1]), function(x){mean(x, na.rm =T)})[,2]
avlength <- aggregate(data$length, by = list(data[,1]), function(x){mean(x, na.rm =T)})[,2]
# Parasite index
sgyr <- 1:nrow(data)
stri <- 1:nrow(data)
sglu <- 1:nrow(data)
scon <- 1:nrow(data)
scysl <- 1:nrow(data)
spro <- 1:nrow(data)
saca <- 1:nrow(data)
scam <- 1:nrow(data)
sang <- 1:nrow(data)
for(j in 1:nrow(data)){
sgyr[j] <- data$Gyr[j]/sd(data$Gyr, na.rm=T)
stri[j] <- data$Tri[j]/sd(data$Tri, na.rm=T)
sglu[j] <- data$Glu[j]/sd(data$Glu, na.rm=T)
scon[j] <- data$Con[j]/sd(data$Con, na.rm=T)
scysl[j] <- data$CysL[j]/sd(data$CysL, na.rm=T)
spro[j] <- data$Pro[j]/sd(data$Pro, na.rm=T)
saca[j] <- data$Aca[j]/sd(data$Aca, na.rm=T)
scam[j] <- data$Cam[j]/sd(data$Cam, na.rm=T)
sang[j] <- data$Ang[j]/sd(data$Ang, na.rm=T)
}
PI <- 1:nrow(data)
for(j in 1:nrow(data)){
PI[j] <- 10/max(sgyr, na.rm=T)*sgyr[j] + 10/max(stri, na.rm=T)*stri[j] + 10/max(sglu, na.rm=T)*sglu[j] +
10/max(scon, na.rm=T)*scon[j] + 10/max(scysl, na.rm=T)*scysl[j] + 10/max(spro, na.rm=T)*spro[j] +
10/max(saca, na.rm=T)*saca[j] + 10/max(scam, na.rm=T)*scam[j] + 10/max(sang, na.rm=T)*sang[j]
}
PI_ecto <- 1:nrow(data)
for(j in 1:nrow(data)){
PI_ecto[j] <- 10/max(sgyr, na.rm=T)*sgyr[j] + 10/max(stri, na.rm=T)*stri[j] + 10/max(sglu, na.rm=T)*sglu[j]
}
PI_endo <- 1:nrow(data)
for(j in 1:nrow(data)){
PI_endo[j] <- 10/max(scon, na.rm=T)*scon[j] + 10/max(scysl, na.rm=T)*scysl[j] + 10/max(spro, na.rm=T)*spro[j] +
10/max(saca, na.rm=T)*saca[j] + 10/max(scam, na.rm=T)*scam[j] + 10/max(sang, na.rm=T)*sang[j]
}
avPI <- aggregate(PI, by = list(data[,1]), function(x){mean(x, na.rm =T)})[,2]
avPI_ecto <- aggregate(PI_ecto, by = list(data[,1]), function(x){mean(x, na.rm =T)})[,2]
avPI_endo <- aggregate(PI_endo, by = list(data[,1]), function(x){mean(x, na.rm =T)})[,2]5 Loading and preparing environmental and spatial data
Physico-chemical data was provided by the Flemish Environmental Agency (VMM). Each fish sampling site was chosen at or near an environmental monitoring site of VMM. Water parameters include water temperature, pH, conductivity, dissolved oxygen (O2), saturation with dissolved oxygen, and Biochemical and Chemical Oxygen Demand (BOD and COD). Nutrient related water parameters include measurements of nitrate (NO3-), nitrite (NO2-), Kjeldahl nitrogen (KjN), total nitrogen (Nt), ammonium (NH4+), and total phosphorus (Pt). Following removal of strong collinear variables (significant correlation with P < 0.05 and Pearson correlation coefficient > |0.6|; Dormann et al., 2013), six environmental physico-chemical variables were retained (temperature, conductivity, COD, saturation with dissolved oxygen, ammonium, and total nitrogen), representing different aspects of water quality. For each water parameter, the average value of the year before sampling was calculated based on monthly monitoring data. Additionally, two hydromorphological variables were included: Tthe presence of a pool-riffle pattern and meanders were noted during field sampling and these parameters were included as binary variables (presence/absence) for a representation of abiotic habitat structure. Spatial (waterway) distances were calculated using the Network Analyst toolbox in ArcGIS. Upstream distance was defined as the maximal upstream distance from the sampling location. Network peripherality was calculated as the average waterway distance of a sampling location to all other locations. Hence, a total of eight environmental and two spatial variables were included in the statistical analysis.
# Environmental data (VMM)
environment <- read.csv("Environment_update.csv", sep=';')
spavar <- read.csv("space2.csv", sep=';') #spatial variables: network centrality and upstream distance
plot(spavar$netcen); plot(density(spavar$netcen))
plot(spavar$updist); plot(density(spavar$updist))
#environmental data (from field observations)
field_data <- read.csv("field_data.csv", sep=',')
environment2 <- cbind(environment[,c(1,49,52:53,55,57,60,63)], field_data[-c(8,10,25,27,37),33:34], spavar[,c(2,3)])
environment2$pool_riffle <- as.factor(environment2$pool_riffle)
environment2$meander <- as.factor(environment2$meander)
netcen <- spavar$netcen
updist <- spavar$updistWe used univariate generalized linear models to investigate how landscape-level effects modify infection patterns of individual parasite taxa, host size and condition. We kept the statistical models linear (as opposed to polynomial) and only considered main effects (i.e. no interaction terms) because we had no prior information from this study system that more complex models were necessary and because the study design with (only) 37 sampling sites was not intended for non-linear models. Ten explanatory variables (temperature, conductivity, COD, saturation with dissolved oxygen, ammonium, total nitrogen, the presence of pool-riffle patterns and meanders, upstream distance, and network peripherality) were included.
6. Univeriate analysis using Bayesian Model Averaging
Univariate analyses - We used generalized linear models in a BMA approach to understand how infection with individual parasite taxa relate to host characteristics (length and condition), environmental conditions as well as spatial distance among sampling sites. Parasite infection was calculated in three ways at the host population level: average abundance (mean parasites per host), prevalence (percentage of infected hosts) and median infection intensity (median number of parasites in infected hosts). We calculated the individual parasitation index (IPI) following Kalbe et al. (2002) as a measurement for total parasite abundance and species richness for each individual fish. This index was calculated for all parasite species combined, and for ecto- and endoparasite species separately. For these models, we assumed a normal error distribution (which appeared to be justified, see Supplementary Figures S1-S2) and applied a Jeffrey-Zellner-Siow prior. Model assumptions (homoscedasticity of the variances and normal distribution of the errors) were assessed using the generic model plot function in R and did onlynot clearly deviate in any of the models for rare parasites. We followed a normal distribution, and not Poisson or negative binomial, for the parasite data for the common species (Trichodina sp. and Gyrodactylus spp.) and the individual parasitation index as the parameters used are deviates from count data. Rare parasites (Glugea, Contracaecum, Anguillicoloides, and unidentified cysts) were excluded from the univariate analysis because there was not enough data to obtain a good fit of the models.For rare parasites (Contracaecum sp. and Anguillicoloides crassus), we used population-level presence-absence data assuming a binomial error distribution and a uniformly distributed BIC prior. Due to low prevalences, the other parasites were not included in the species-specific models. Explanatory variables were considered important when they had a posterior inclusion probability (PIP) of 0.5. To account for overdispersion in the parasite counts, we transformed the data by downweighting overdispersed taxa following Clarke et al. (2006) using the dispweight function in the R package vegan v2.5.6 (Oksanen et al., 2013).
6.0.1 Figure
library(BAS)
# Make a matrix for PIP (Posterior Inclusion Probability)
PIP <- matrix(nrow=12, ncol=14)
rownames(PIP) <- c("Host condition", "Host size", "Temperature", "Oxygen saturation", "Conductivity", "COD", "Ammonium", "Total nitrogen", "Pool riffle pattern", "Meander", "Network peripherality", "Upstream distance")
colnames(PIP) <- c("Host condition", "Host size", "Gyrodactylus abundance", "Gyrodactylus prevalence", "Gyrodactylus infection intensity", "Trichodina abundance", "Trichodina prevalence", "Trichodina infection intensity", "Glugea", "Contracaecum", "Aguillicola",
"PI", "PI ecto", "PI endo") #Condition
bas.model <- bas.lm(avcondition ~ T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
coef.model <- coef(bas.model)
pip <- summary(bas.model)
PIP[c(3:12),1] <- pip[2:11,1]*sign(coef.model$postmean[2:11])
#Length
bas.model <- bas.lm(avlength ~ T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
coef.model <- coef(bas.model)
pip <- summary(bas.model)
PIP[c(3:12),2] <- pip[2:11,1]*sign(coef.model$postmean[2:11])
#Gyrodactylus abundance
bas.model <- bas.lm(avab$Gyr ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
coef.model <- coef(bas.model)
pip <- summary(bas.model)
PIP[c(1:12),3] <- pip[2:13,1]*sign(coef.model$postmean[2:13])
#Gyrodactylus prevalence
bas.model <- bas.lm(prev$Gyr ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
coef.model <- coef(bas.model)
pip <- summary(bas.model)
PIP[c(1:12),4] <- pip[2:13,1]*sign(coef.model$postmean[2:13])
#Gyrodactylus infection intensity
bas.model <- bas.lm(medin$Gyr ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
coef.model <- coef(bas.model)
pip <- summary(bas.model)
PIP[c(1:12),5] <- pip[2:13,1]*sign(coef.model$postmean[2:13])
#Trichodina abundance
bas.model <- bas.lm(avab$Tri ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
coef.model <- coef(bas.model)
pip <- summary(bas.model)
PIP[c(1:12),6] <- pip[2:13,1]*sign(coef.model$postmean[2:13])
#Trichodina prevalence
bas.model <- bas.lm(prev$Tri ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
coef.model <- coef(bas.model)
pip <- summary(bas.model)
PIP[c(1:12),7] <- pip[2:13,1]*sign(coef.model$postmean[2:13])
#Trichodina infection intensity
bas.model <- bas.lm(medin$Tri ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
coef.model <- coef(bas.model)
pip <- summary(bas.model)
PIP[c(1:12),8] <- pip[2:13,1]*sign(coef.model$postmean[2:13])
#Glugea
bas.model <- bas.glm(pa$Glu ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, betaprior=g.prior(100), family=binomial)
coef.model <- coef(bas.model)## Warning in object$prior == "AIC" || object$prior == "BIC": 'length(x) = 220 > 1'
## in coercion to 'logical(1)'
## Warning in object$prior == "AIC" || object$prior == "BIC": 'length(x) = 220 > 1'
## in coercion to 'logical(1)'pip <- summary(bas.model)
PIP[c(1:12),9] <- pip[2:13,1]*sign(coef.model$postmean[2:13])
#Contracaecum
bas.model <- bas.glm(pa$Con ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, betaprior=g.prior(100), family=binomial)
coef.model <- coef(bas.model)## Warning in object$prior == "AIC" || object$prior == "BIC": 'length(x) = 234 > 1'
## in coercion to 'logical(1)'## Warning in object$prior == "AIC" || object$prior == "BIC": 'length(x) = 234 > 1'
## in coercion to 'logical(1)'pip <- summary(bas.model)
PIP[c(1:12),10] <- pip[2:13,1]*sign(coef.model$postmean[2:13])
#Anguillicola
bas.model <- bas.glm(pa$Ang ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, betaprior=g.prior(100), family=binomial)
coef.model <- coef(bas.model)## Warning in object$prior == "AIC" || object$prior == "BIC": 'length(x) = 371 > 1'
## in coercion to 'logical(1)'## Warning in object$prior == "AIC" || object$prior == "BIC": 'length(x) = 371 > 1'
## in coercion to 'logical(1)'pip <- summary(bas.model)
PIP[c(1:12),11] <- pip[2:13,1]*sign(coef.model$postmean[2:13])
#PI
bas.model <- bas.lm(avPI ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
coef.model <- coef(bas.model)
pip <- summary(bas.model)
PIP[c(1:12),12] <- pip[2:13,1]*sign(coef.model$postmean[2:13])
#PI ecto
bas.model <- bas.lm(avPI_ecto ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
coef.model <- coef(bas.model)
pip <- summary(bas.model)
PIP[c(1:12),13] <- pip[2:13,1]*sign(coef.model$postmean[2:13])
#PI endo
bas.model <- bas.lm(avPI_endo ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
coef.model <- coef(bas.model)
pip <- summary(bas.model)
PIP[c(1:12),14] <- pip[2:13,1]*sign(coef.model$postmean[2:13])library(gplots)##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowessx = round(PIP, digits=2)
x[abs(PIP)<0.5] <- ""
x[abs(PIP)>0.5] <- "+"
heatmap.2(PIP[,-c(9,10,11)],
cellnote = x[,-c(9,10,11)],
#main = "Correlation",
notecex=1,
notecol="white",
density.info="none",
trace="none",
margins =c(10,8),
col=redblue(256),
dendrogram="both",
cexRow = 0.7,
cexCol = 0.7,
key.title = "PIP",
lhei = c(1,3),
lwid = c(0.5, 0.5),
#Colv="NA"
) 
6.1 Variation in host condition
bas.model <- bas.lm(avcondition ~ T_av + O2_sat_av + Con_av + COD_av + NH4._av + Nt_av +
pool_riffle + meander + netcen + updist,
data=environment2, prior="JZS")
plot(bas.model)
summary(bas.model)## P(B != 0 | Y) model 1 model 2 model 3 model 4 model 5
## Intercept 1.00000000 1.0000 1.0000000 1.0000000 1.0000000 1.0000000
## T_av 0.12386945 0.0000 1.0000000 0.0000000 0.0000000 0.0000000
## O2_sat_av 0.05073157 0.0000 0.0000000 0.0000000 0.0000000 0.0000000
## Con_av 0.03919858 0.0000 0.0000000 0.0000000 0.0000000 0.0000000
## COD_av 0.05197281 0.0000 0.0000000 0.0000000 0.0000000 1.0000000
## NH4._av 0.04846220 0.0000 0.0000000 0.0000000 0.0000000 0.0000000
## Nt_av 0.05248386 0.0000 0.0000000 0.0000000 0.0000000 0.0000000
## pool_riffle1 0.06729856 0.0000 0.0000000 0.0000000 1.0000000 0.0000000
## meander1 0.06527705 0.0000 0.0000000 0.0000000 0.0000000 0.0000000
## netcen 0.06898689 0.0000 0.0000000 1.0000000 0.0000000 0.0000000
## updist 0.04496936 0.0000 0.0000000 0.0000000 0.0000000 0.0000000
## BF NA 1.0000 0.6511099 0.3462387 0.2957714 0.2212089
## PostProbs NA 0.6912 0.0450000 0.0239000 0.0204000 0.0153000
## R2 NA 0.0000 0.0906000 0.0565000 0.0478000 0.0315000
## dim NA 1.0000 2.0000000 2.0000000 2.0000000 2.0000000
## logmarg NA 0.0000 -0.4290769 -1.0606268 -1.2181684 -1.5086476image(bas.model, rotate=F)
coef.model <- coef(bas.model)
#abs(coef.model$postmean)-2*coef.model$postsd > 0
plot(confint(coef.model, parm = 2:11))## Warning in arrows(x[not.deg], ci[not.deg, 1], x[not.deg], ci[not.deg, 2], :
## zero-length arrow is of indeterminate angle and so skipped
## NULLconfint <- confint(coef.model, parm = 2:11)
write.table(confint, 'condition.txt', sep="\t")
pip <- summary(bas.model)
PIP[c(3:12),1] <- pip[2:11,1]*sign(coef.model$postmean[2:11])
#coef.model$postmean[2:11]6.2 Variation in host length
bas.model <- bas.lm(avlength ~ T_av + O2_sat_av + Con_av + COD_av + NH4._av + Nt_av +
pool_riffle + meander + netcen + updist,
data=environment2, prior="JZS")
plot(bas.model)
summary(bas.model)## P(B != 0 | Y) model 1 model 2 model 3 model 4 model 5
## Intercept 1.0000000 1.0000000 1.00000000 1.0000000 1.000000 1.0000000
## T_av 0.1914598 0.0000000 0.00000000 0.0000000 0.000000 0.0000000
## O2_sat_av 0.1407247 0.0000000 0.00000000 0.0000000 0.000000 0.0000000
## Con_av 0.4373524 0.0000000 0.00000000 1.0000000 1.000000 0.0000000
## COD_av 0.1445232 0.0000000 0.00000000 0.0000000 0.000000 0.0000000
## NH4._av 0.2026061 0.0000000 0.00000000 0.0000000 0.000000 0.0000000
## Nt_av 0.1889236 0.0000000 0.00000000 0.0000000 0.000000 0.0000000
## pool_riffle1 0.2259504 0.0000000 0.00000000 0.0000000 0.000000 1.0000000
## meander1 0.3217534 0.0000000 0.00000000 0.0000000 1.000000 0.0000000
## netcen 0.6121912 1.0000000 0.00000000 0.0000000 0.000000 1.0000000
## updist 0.1480063 0.0000000 0.00000000 0.0000000 0.000000 0.0000000
## BF NA 0.8278694 0.07277547 0.2894406 1.000000 0.6785609
## PostProbs NA 0.1582000 0.13910000 0.0553000 0.042500 0.0288000
## R2 NA 0.2306000 0.00000000 0.1819000 0.314300 0.2982000
## dim NA 2.0000000 1.00000000 2.0000000 3.000000 3.0000000
## logmarg NA 2.4314765 0.00000000 1.3805712 2.620376 2.2325953image(bas.model, rotate=F)
coef.model <- coef(bas.model)
#abs(coef.model$postmean)-2*coef.model$postsd > 0
plot(confint(coef.model, parm = 2:11))## Warning in arrows(x[not.deg], ci[not.deg, 1], x[not.deg], ci[not.deg, 2], :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(x[not.deg], ci[not.deg, 1], x[not.deg], ci[not.deg, 2], :
## zero-length arrow is of indeterminate angle and so skipped
## NULLconfint <- confint(coef.model, parm = 2:11)
write.table(confint, 'length.txt', sep="\t")
pip <- summary(bas.model)
PIP[c(3:12),1] <- pip[2:11,1]*sign(coef.model$postmean[2:11])
#coef.model$postmean[2:11]
# Prediction plot
newdata = as.data.frame(cbind(rep(mean(environment2$T_av), 37),
rep(mean(environment2$O2_sat_av), 37),
rep(mean(environment2$Con_av), 37),
rep(mean(environment2$COD_av), 37),
rep(mean(environment2$NH4._av), 37),
rep(mean(environment2$Nt_av), 37),
rep(1, 37),
rep(1, 37),
rep(mean(netcen), 37),
rep(mean(updist), 37)))
colnames(newdata) <- c("T_av", "O2_sat_av", "Con_av", "COD_av", "NH4._av", "Nt_av", "pool_riffle", "meander", "netcen", "updist")
newdata[,"pool_riffle"] <- as.factor(newdata[,"pool_riffle"]); newdata[,"meander"] <- as.factor(newdata[,"meander"])
newdata1 <- newdata; newdata1[,"netcen"] <- netcen
BMA <- predict(bas.model, newdata = newdata1, estimator = "BMA", se.fit=TRUE)
png(file="figure.png", res=600, width=3000, height=3000)
library(ggplot2)
figure_avlength <- ggplot(environment2, aes(netcen, BMA$fit)) +
theme_bw() +
geom_line(color="red", size=1) +
geom_ribbon(aes(ymin = (BMA$fit-BMA$se.bma.fit), ymax = (BMA$fit+BMA$se.bma.fit)), alpha = .1) +
geom_point(data = environment2, aes(x=netcen, y=avlength)) +
labs(x=expression("Network peripherality [m]"), y=expression("Average host length [mm]")) +
theme(axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12)) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.dev.off()## png
## 2figure_avlength
6.3 Variation in Gyrodactylus infection
6.3.1 Mean abundance
bas.model <- bas.lm(avab$Gyr ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
yhat = fitted(bas.model, estimator = "BMA") #these are the fitted values under BMA
r = bas.model$Y - yhat #these are the model residuals
plot(bas.model)
summary(bas.model)## P(B != 0 | Y) model 1 model 2 model 3 model 4 model 5
## Intercept 1.0000000 1.000000 1.00000000 1.0000000 1.0000000 1.0000000
## avlength 0.5500651 1.000000 0.00000000 0.0000000 0.0000000 1.0000000
## avcondition 0.3049274 0.000000 0.00000000 0.0000000 0.0000000 1.0000000
## T_av 0.1620852 0.000000 0.00000000 0.0000000 0.0000000 0.0000000
## O2_sat_av 0.2017699 0.000000 0.00000000 0.0000000 0.0000000 0.0000000
## Con_av 0.2621665 0.000000 0.00000000 0.0000000 0.0000000 0.0000000
## COD_av 0.8086300 1.000000 0.00000000 1.0000000 1.0000000 1.0000000
## NH4._av 0.2417809 0.000000 0.00000000 0.0000000 0.0000000 0.0000000
## Nt_av 0.8618345 1.000000 1.00000000 1.0000000 1.0000000 1.0000000
## pool_riffle1 0.1760484 0.000000 0.00000000 0.0000000 0.0000000 0.0000000
## meander1 0.1775842 0.000000 0.00000000 0.0000000 0.0000000 0.0000000
## netcen 0.3006370 0.000000 0.00000000 1.0000000 0.0000000 0.0000000
## updist 0.1648814 0.000000 0.00000000 0.0000000 0.0000000 0.0000000
## BF NA 1.000000 0.03902505 0.5452757 0.1537678 0.8271829
## PostProbs NA 0.072100 0.05160000 0.0393000 0.0369000 0.0265000
## R2 NA 0.524000 0.28790000 0.5059000 0.4100000 0.5631000
## dim NA 4.000000 2.00000000 4.0000000 3.0000000 5.0000000
## logmarg NA 6.997337 3.75378552 6.3908733 5.1250253 6.8076077image(bas.model, rotate=F)
coef.model <- coef(bas.model)
abs(coef.model$postmean)-2*coef.model$postsd > 0## [1] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSEconfint(coef.model)## 2.5% 97.5% beta
## Intercept 9.665145e-02 2.035129e-01 1.489134e-01
## avlength -2.510048e-02 3.292762e-05 -8.122675e-03
## avcondition -1.018870e+00 1.285004e-03 -1.500326e-01
## T_av -1.817945e-02 1.869386e-02 -1.493132e-04
## O2_sat_av -1.357600e-03 5.206338e-03 3.794809e-04
## Con_av -8.116626e-05 5.132180e-04 5.726233e-05
## COD_av 0.000000e+00 1.283859e-02 6.652983e-03
## NH4._av -7.496342e-02 5.641334e-02 -1.888173e-04
## Nt_av 0.000000e+00 5.236430e-02 2.930918e-02
## pool_riffle1 -5.509604e-02 8.247650e-02 3.966550e-03
## meander1 -1.013261e-01 4.005564e-02 -4.806259e-03
## netcen -4.520712e-07 1.020908e-05 1.332021e-06
## updist -1.263626e-06 1.831485e-06 4.618571e-08
## attr(,"Probability")
## [1] 0.95
## attr(,"class")
## [1] "confint.bas"plot(confint(coef.model, parm = 2:11))
## NULLconfint <- confint(coef.model, parm = 2:11)
write.table(confint, 'GyroAA.txt', sep="\t")
pip <- summary(bas.model)
PIP[c(1:12),3] <- pip[2:13,1]*sign(coef.model$postmean[2:13])6.3.1.1 Prediction plot for marginal effect of host condition on average Gyrodactylus infection
# Prediction plot
newdata = as.data.frame(cbind(rep(mean(avlength), 37),
rep(mean(avcondition), 37),
rep(mean(environment2$T_av), 37),
rep(mean(environment2$O2_sat_av), 37),
rep(mean(environment2$Con_av), 37),
rep(mean(environment2$COD_av), 37),
rep(mean(environment2$NH4._av), 37),
rep(mean(environment2$Nt_av), 37),
rep(1, 37),
rep(1, 37),
rep(mean(netcen), 37),
rep(mean(updist), 37)))
colnames(newdata) <- c("avlength", "avcondition", "T_av", "O2_sat_av", "Con_av", "COD_av", "NH4._av", "Nt_av", "pool_riffle", "meander", "netcen", "updist")
newdata[,"pool_riffle"] <- as.factor(newdata[,"pool_riffle"]); newdata[,"meander"] <- as.factor(newdata[,"meander"])
newdata1 <- newdata; newdata1[,"avcondition"] <- avcondition
BMA <- predict(bas.model, newdata = newdata1, estimator = "BMA", se.fit=TRUE)
ggplot(environment2, aes(avcondition, BMA$fit)) +
theme_bw() +
geom_line(color="red", size=1) +
geom_ribbon(aes(ymin = (BMA$fit-BMA$se.bma.fit), ymax = (BMA$fit+BMA$se.bma.fit)), alpha = .1) +
geom_point(data = environment2, aes(x=avcondition, y=avab$Gyr)) +
labs(x=expression("Average host condition"), y=expression("Average Gyrodactylus count")) +
theme(axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12)) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
6.3.1.2 Prediction plot for marginal effect of COD on average Gyrodactylus infection
newdata1 <- newdata; newdata1[,"COD_av"] <- environment2$COD_av
BMA <- predict(bas.model, newdata = newdata1, estimator = "BMA", se.fit=TRUE)
ggplot(environment2, aes(COD_av, BMA$fit)) +
theme_bw() +
geom_line(color="red", size=1) +
geom_ribbon(aes(ymin = (BMA$fit-BMA$se.bma.fit), ymax = (BMA$fit+BMA$se.bma.fit)), alpha = .1) +
geom_point(data = environment2, aes(x=COD_av, y=avab$Gyr)) +
labs(x=expression("COD"), y=expression("Average Gyrodactylus count")) +
theme(axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12)) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
6.3.2.3 Prediction plot for marginal effect of total nitrogen on average Gyrodactylus infection
newdata1 <- newdata; newdata1[,"Nt_av"] <- environment2$Nt_av
BMA <- predict(bas.model, newdata = newdata1, estimator = "BMA", se.fit=TRUE)
ggplot(environment2, aes(Nt_av, BMA$fit)) +
theme_bw() +
geom_line(color="red", size=1) +
geom_ribbon(aes(ymin = (BMA$fit-BMA$se.bma.fit), ymax = (BMA$fit+BMA$se.bma.fit)), alpha = .1) +
geom_point(data = environment2, aes(x=Nt_av, y=avab$Gyr)) +
labs(x=expression("Nt"), y=expression("Average Gyrodactylus count")) +
theme(axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12)) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
6.3.2 Median infection intensity
bas.model <- bas.lm(medin$Gyr ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
yhat = fitted(bas.model, estimator = "BMA") #these are the fitted values under BMA
r = bas.model$Y - yhat #these are the model residuals
library(car)## Loading required package: carDataplot(bas.model)
summary(bas.model)## P(B != 0 | Y) model 1 model 2 model 3 model 4 model 5
## Intercept 1.00000000 1.0000 1.0000000 1.000000 1.0000000 1.0000000
## avlength 0.02065469 0.0000 0.0000000 0.000000 0.0000000 0.0000000
## avcondition 0.04286905 0.0000 1.0000000 0.000000 0.0000000 0.0000000
## T_av 0.02991678 0.0000 0.0000000 0.000000 0.0000000 0.0000000
## O2_sat_av 0.02400544 0.0000 0.0000000 0.000000 0.0000000 0.0000000
## Con_av 0.02143314 0.0000 0.0000000 0.000000 0.0000000 0.0000000
## COD_av 0.03655302 0.0000 0.0000000 0.000000 1.0000000 0.0000000
## NH4._av 0.03250611 0.0000 0.0000000 0.000000 0.0000000 1.0000000
## Nt_av 0.04088810 0.0000 0.0000000 1.000000 0.0000000 0.0000000
## pool_riffle1 0.02307275 0.0000 0.0000000 0.000000 0.0000000 0.0000000
## meander1 0.02038443 0.0000 0.0000000 0.000000 0.0000000 0.0000000
## netcen 0.02173649 0.0000 0.0000000 0.000000 0.0000000 0.0000000
## updist 0.02738794 0.0000 0.0000000 0.000000 0.0000000 0.0000000
## BF NA 1.0000 0.3176354 0.302852 0.2488803 0.2411373
## PostProbs NA 0.7775 0.0206000 0.019600 0.0161000 0.0156000
## R2 NA 0.0000 0.0517000 0.049100 0.0381000 0.0363000
## dim NA 1.0000 2.0000000 2.000000 2.0000000 2.0000000
## logmarg NA 0.0000 -1.1468512 -1.194511 -1.3907833 -1.4223890image(bas.model, rotate=F)
coef.model <- coef(bas.model)
abs(coef.model$postmean)-2*coef.model$postsd > 0## [1] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSEconfint(coef.model)## 2.5% 97.5% beta
## Intercept 1.560904 3.240035 2.405405e+00
## avlength 0.000000 0.000000 -5.938396e-04
## avcondition 0.000000 0.000000 -2.939823e-01
## T_av 0.000000 0.000000 -7.368692e-03
## O2_sat_av 0.000000 0.000000 -4.581964e-04
## Con_av 0.000000 0.000000 -1.594570e-05
## COD_av 0.000000 0.000000 1.609035e-03
## NH4._av 0.000000 0.000000 1.027436e-02
## Nt_av 0.000000 0.000000 7.570653e-03
## pool_riffle1 0.000000 0.000000 1.051497e-02
## meander1 0.000000 0.000000 2.511911e-03
## netcen 0.000000 0.000000 3.990331e-07
## updist 0.000000 0.000000 4.560731e-07
## attr(,"Probability")
## [1] 0.95
## attr(,"class")
## [1] "confint.bas"plot(confint(coef.model, parm = 2:11))
## NULLconfint <- confint(coef.model, parm = 2:11)
write.table(confint, 'GyroAA.txt', sep="\t")
pip <- summary(bas.model)
PIP[c(1:12),3] <- pip[2:13,1]*sign(coef.model$postmean[2:13])6.3.2 Prevalence
bas.model <- bas.lm(prev$Gyr ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
yhat = fitted(bas.model, estimator = "BMA") #these are the fitted values under BMA
r = bas.model$Y - yhat #these are the model residuals
library(car)
plot(bas.model)
summary(bas.model)## P(B != 0 | Y) model 1 model 2 model 3 model 4 model 5
## Intercept 1.00000000 1.000000 1.00000000 1.0000000 1.0000000 1.00000000
## avlength 0.16685753 0.000000 0.00000000 0.0000000 1.0000000 0.00000000
## avcondition 0.07728991 0.000000 0.00000000 0.0000000 0.0000000 0.00000000
## T_av 0.07242807 0.000000 0.00000000 0.0000000 0.0000000 0.00000000
## O2_sat_av 0.09667222 0.000000 0.00000000 0.0000000 0.0000000 0.00000000
## Con_av 0.58076129 1.000000 0.00000000 1.0000000 0.0000000 0.00000000
## COD_av 0.17268847 0.000000 0.00000000 1.0000000 0.0000000 0.00000000
## NH4._av 0.09134534 0.000000 0.00000000 0.0000000 0.0000000 0.00000000
## Nt_av 0.11056039 0.000000 0.00000000 0.0000000 0.0000000 0.00000000
## pool_riffle1 0.13922799 0.000000 0.00000000 0.0000000 0.0000000 0.00000000
## meander1 0.09905733 0.000000 0.00000000 0.0000000 0.0000000 0.00000000
## netcen 0.11864979 0.000000 0.00000000 0.0000000 0.0000000 1.00000000
## updist 0.06837623 0.000000 0.00000000 0.0000000 0.0000000 0.00000000
## BF NA 1.000000 0.08283414 0.8857497 0.1246735 0.07409002
## PostProbs NA 0.228800 0.22750000 0.0369000 0.0285000 0.01700000
## R2 NA 0.233300 0.00000000 0.3039000 0.1341000 0.10730000
## dim NA 2.000000 1.00000000 3.0000000 2.0000000 2.00000000
## logmarg NA 2.490915 0.00000000 2.3695941 0.4088580 -0.11155941image(bas.model, rotate=F)
coef.model <- coef(bas.model)
abs(coef.model$postmean)-2*coef.model$postsd > 0## [1] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSEconfint(coef.model)## 2.5% 97.5% beta
## Intercept 2.490570e-01 3.923215e-01 3.224082e-01
## avlength -2.272114e-02 0.000000e+00 -2.431148e-03
## avcondition -4.217010e-01 1.626800e-02 -1.986363e-02
## T_av -1.743285e-02 1.850250e-03 -7.123130e-04
## O2_sat_av -4.604791e-03 3.272811e-05 -2.662986e-04
## Con_av 0.000000e+00 8.017249e-04 3.112530e-04
## COD_av 0.000000e+00 8.298397e-03 9.376253e-04
## NH4._av -3.741585e-04 4.694160e-02 1.064409e-03
## Nt_av -1.246419e-04 2.499388e-02 1.612509e-03
## pool_riffle1 0.000000e+00 1.537665e-01 1.465705e-02
## meander1 -1.130552e-01 3.190549e-04 -6.966751e-03
## netcen 0.000000e+00 8.426670e-06 5.765326e-07
## updist -1.059535e-06 1.015335e-07 -6.922933e-09
## attr(,"Probability")
## [1] 0.95
## attr(,"class")
## [1] "confint.bas"plot(confint(coef.model, parm = 2:11))
## NULLconfint <- confint(coef.model, parm = 2:11)
write.table(confint, 'GyroAA.txt', sep="\t")
pip <- summary(bas.model)
PIP[c(1:12),3] <- pip[2:13,1]*sign(coef.model$postmean[2:13])6.3.2.3 Prediction plot for marginal effect of conductivity on average Gyrodactylus infection
newdata1 <- newdata; newdata1[,"Con_av"] <- environment2$Con_av
BMA <- predict(bas.model, newdata = newdata1, estimator = "BMA", se.fit=TRUE)
ggplot(environment2, aes(Con_av, BMA$fit)) +
theme_bw() +
geom_line(color="red", size=1) +
geom_ribbon(aes(ymin = (BMA$fit-BMA$se.bma.fit), ymax = (BMA$fit+BMA$se.bma.fit)), alpha = .1) +
geom_point(data = environment2, aes(x=Con_av, y=prev$Gyr)) +
labs(x=expression("Conductivity"), y=expression("Gyrodactylus prevalence")) +
theme(axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12)) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
6.4 Variation in Trichodina infection
6.4.1 Mean abundance
bas.model <- bas.lm(avab$Tri ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
yhat = fitted(bas.model, estimator = "BMA") #these are the fitted values under BMA
r = bas.model$Y - yhat #these are the model residuals
plot(bas.model)
summary(bas.model)## P(B != 0 | Y) model 1 model 2 model 3 model 4 model 5
## Intercept 1.00000000 1.0000000 1.000000 1.000000 1.0000000 1.0000000
## avlength 0.12444042 0.0000000 0.000000 0.000000 0.0000000 0.0000000
## avcondition 0.09559152 0.0000000 0.000000 0.000000 0.0000000 0.0000000
## T_av 0.08500185 0.0000000 0.000000 0.000000 0.0000000 0.0000000
## O2_sat_av 0.09751169 0.0000000 0.000000 0.000000 0.0000000 0.0000000
## Con_av 0.42180029 0.0000000 1.000000 0.000000 0.0000000 0.0000000
## COD_av 0.46724539 0.0000000 1.000000 0.000000 0.0000000 1.0000000
## NH4._av 0.21465122 0.0000000 0.000000 0.000000 1.0000000 0.0000000
## Nt_av 0.34576751 0.0000000 0.000000 1.000000 0.0000000 1.0000000
## pool_riffle1 0.13743699 0.0000000 0.000000 0.000000 0.0000000 0.0000000
## meander1 0.08968689 0.0000000 0.000000 0.000000 0.0000000 0.0000000
## netcen 0.10313787 0.0000000 0.000000 0.000000 0.0000000 0.0000000
## updist 0.08604777 0.0000000 0.000000 0.000000 0.0000000 0.0000000
## BF NA 0.0239404 1.000000 0.170643 0.1105942 0.2711811
## PostProbs NA 0.1650000 0.104400 0.098000 0.0635000 0.0283000
## R2 NA 0.0000000 0.358500 0.209300 0.1890000 0.3063000
## dim NA 1.0000000 3.000000 2.000000 2.0000000 3.0000000
## logmarg NA 0.0000000 3.732188 1.964006 1.5303004 2.4272192image(bas.model, rotate=F)
coef.model <- coef(bas.model)
abs(coef.model$postmean)-2*coef.model$postsd > 0## [1] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSEconfint(coef.model)## 2.5% 97.5% beta
## Intercept 1.229058e-01 2.721520e-01 2.002413e-01
## avlength -1.582818e-02 1.162016e-03 -1.114390e-03
## avcondition -4.894906e-01 1.211993e-01 -2.394139e-02
## T_av -1.296026e-02 1.183449e-02 -1.479738e-04
## O2_sat_av -1.315738e-03 4.296851e-03 1.132458e-04
## Con_av -1.743950e-08 7.711874e-04 2.109076e-04
## COD_av -1.858816e-06 1.395582e-02 4.203136e-03
## NH4._av -2.507796e-05 1.100733e-01 1.096330e-02
## Nt_av -2.448514e-05 4.941906e-02 1.044647e-02
## pool_riffle1 0.000000e+00 1.490229e-01 1.212839e-02
## meander1 -7.177485e-02 2.598627e-02 -1.678368e-03
## netcen -6.140315e-06 1.237054e-06 -2.846749e-07
## updist -1.003797e-06 9.794545e-07 -1.915234e-08
## attr(,"Probability")
## [1] 0.95
## attr(,"class")
## [1] "confint.bas"plot(confint(coef.model, parm = 2:11))
## NULLconfint <- confint(coef.model, parm = 2:11)
write.table(confint, 'GyroAA.txt', sep="\t")
pip <- summary(bas.model)
PIP[c(1:12),3] <- pip[2:13,1]*sign(coef.model$postmean[2:13])6.4.1 Median infection intensity
bas.model <- bas.lm(medin$Tri ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
yhat = fitted(bas.model, estimator = "BMA") #these are the fitted values under BMA
r = bas.model$Y - yhat #these are the model residuals
plot(bas.model)
summary(bas.model)## P(B != 0 | Y) model 1 model 2 model 3 model 4 model 5
## Intercept 1.0000000 1.000000 1.00000000 1.0000000 1.0000000 1.0000000
## avlength 0.2881774 0.000000 0.00000000 0.0000000 0.0000000 0.0000000
## avcondition 0.2224163 0.000000 0.00000000 0.0000000 1.0000000 0.0000000
## T_av 0.1574615 0.000000 0.00000000 0.0000000 0.0000000 0.0000000
## O2_sat_av 0.1621652 0.000000 0.00000000 0.0000000 0.0000000 0.0000000
## Con_av 0.7912882 1.000000 0.00000000 1.0000000 1.0000000 1.0000000
## COD_av 0.7219333 1.000000 0.00000000 0.0000000 1.0000000 1.0000000
## NH4._av 0.3248706 0.000000 1.00000000 1.0000000 0.0000000 0.0000000
## Nt_av 0.3218716 0.000000 0.00000000 0.0000000 0.0000000 0.0000000
## pool_riffle1 0.2081402 0.000000 0.00000000 0.0000000 0.0000000 0.0000000
## meander1 0.2655250 0.000000 0.00000000 0.0000000 0.0000000 1.0000000
## netcen 0.2107764 0.000000 0.00000000 0.0000000 0.0000000 0.0000000
## updist 0.1536937 0.000000 0.00000000 0.0000000 0.0000000 0.0000000
## BF NA 1.000000 0.04957628 0.2279222 0.5092549 0.4837322
## PostProbs NA 0.148800 0.04060000 0.0339000 0.0227000 0.0216000
## R2 NA 0.449900 0.26810000 0.3987000 0.4816000 0.4799000
## dim NA 3.000000 2.00000000 3.0000000 4.0000000 4.0000000
## logmarg NA 6.288599 3.28435583 4.8098478 5.6137920 5.5623749image(bas.model, rotate=F)
coef.model <- coef(bas.model)
abs(coef.model$postmean)-2*coef.model$postsd > 0## [1] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSEconfint(coef.model)## 2.5% 97.5% beta
## Intercept 1.695452e+01 3.438593e+01 2.547297e+01
## avlength -3.380690e+00 2.731409e-01 -5.158998e-01
## avcondition -1.324481e+02 1.400968e+01 -1.375058e+01
## T_av -2.029153e+00 4.134904e+00 2.065105e-01
## O2_sat_av -5.947346e-01 4.319307e-01 -1.498026e-02
## Con_av 0.000000e+00 1.283100e-01 6.585727e-02
## COD_av 0.000000e+00 1.995554e+00 9.195418e-01
## NH4._av -2.244992e+00 1.596905e+01 2.222211e+00
## Nt_av -7.694817e-02 6.286264e+00 1.012166e+00
## pool_riffle1 -2.263931e+00 2.328611e+01 2.247962e+00
## meander1 -2.711917e+01 2.602300e-01 -3.523477e+00
## netcen -1.375132e-03 2.494413e-04 -1.366046e-04
## updist -2.245534e-04 2.760688e-04 5.653370e-06
## attr(,"Probability")
## [1] 0.95
## attr(,"class")
## [1] "confint.bas"plot(confint(coef.model, parm = 2:11))
## NULLconfint <- confint(coef.model, parm = 2:11)
write.table(confint, 'GyroAA.txt', sep="\t")
pip <- summary(bas.model)
PIP[c(1:12),3] <- pip[2:13,1]*sign(coef.model$postmean[2:13])6.3.2.3 Prediction plot for marginal effect of conductivity on median infection intensity with Trichodina
newdata1 <- newdata; newdata1[,"Con_av"] <- environment2$Con_av
BMA <- predict(bas.model, newdata = newdata1, estimator = "BMA", se.fit=TRUE)
ggplot(environment2, aes(Con_av, BMA$fit)) +
theme_bw() +
geom_line(color="red", size=1) +
geom_ribbon(aes(ymin = (BMA$fit-BMA$se.bma.fit), ymax = (BMA$fit+BMA$se.bma.fit)), alpha = .1) +
geom_point(data = environment2, aes(x=Con_av, y=medin$Tri)) +
labs(x=expression("Conductivity"), y=expression("Trichodina median infection intensity")) +
theme(axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12)) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
6.3.2.3 Prediction plot for marginal effect of COD on median infection intensity with Trichodina
newdata1 <- newdata; newdata1[,"COD_av"] <- environment2$COD_av
BMA <- predict(bas.model, newdata = newdata1, estimator = "BMA", se.fit=TRUE)
ggplot(environment2, aes(COD_av, BMA$fit)) +
theme_bw() +
geom_line(color="red", size=1) +
geom_ribbon(aes(ymin = (BMA$fit-BMA$se.bma.fit), ymax = (BMA$fit+BMA$se.bma.fit)), alpha = .1) +
geom_point(data = environment2, aes(x=COD_av, y=medin$Tri)) +
labs(x=expression("Conductivity"), y=expression("Trichodina median infection intensity")) +
theme(axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12)) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
6.4.1 Prevalence
bas.model <- bas.lm(prev$Tri ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
yhat = fitted(bas.model, estimator = "BMA") #these are the fitted values under BMA
r = bas.model$Y - yhat #these are the model residuals
plot(bas.model)
summary(bas.model)## P(B != 0 | Y) model 1 model 2 model 3 model 4 model 5
## Intercept 1.00000000 1.0000000 1.0000000 1.0000000 1.0000000 1.000000
## avlength 0.04005815 0.0000000 0.0000000 0.0000000 0.0000000 0.000000
## avcondition 0.03389439 0.0000000 0.0000000 0.0000000 0.0000000 0.000000
## T_av 0.03654853 0.0000000 0.0000000 0.0000000 0.0000000 0.000000
## O2_sat_av 0.03749123 0.0000000 0.0000000 0.0000000 0.0000000 0.000000
## Con_av 0.20573471 0.0000000 1.0000000 0.0000000 1.0000000 0.000000
## COD_av 0.04629842 0.0000000 0.0000000 0.0000000 0.0000000 0.000000
## NH4._av 0.06690739 0.0000000 0.0000000 1.0000000 0.0000000 0.000000
## Nt_av 0.04604656 0.0000000 0.0000000 0.0000000 0.0000000 1.000000
## pool_riffle1 0.03411173 0.0000000 0.0000000 0.0000000 0.0000000 0.000000
## meander1 0.03657952 0.0000000 0.0000000 0.0000000 0.0000000 0.000000
## netcen 0.07467299 0.0000000 0.0000000 0.0000000 1.0000000 0.000000
## updist 0.07352673 0.0000000 0.0000000 0.0000000 0.0000000 0.000000
## BF NA 0.5616705 0.7274201 0.2732671 1.0000000 0.155205
## PostProbs NA 0.6354000 0.0686000 0.0258000 0.0171000 0.014600
## R2 NA 0.0000000 0.1264000 0.0750000 0.2249000 0.044000
## dim NA 1.0000000 2.0000000 2.0000000 3.0000000 2.000000
## logmarg NA 0.0000000 0.2585887 -0.7204656 0.5768398 -1.286169image(bas.model, rotate=F)
coef.model <- coef(bas.model)
abs(coef.model$postmean)-2*coef.model$postsd > 0## [1] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSEconfint(coef.model)## 2.5% 97.5% beta
## Intercept 7.801829e-01 8.849431e-01 8.340580e-01
## avlength 0.000000e+00 0.000000e+00 -1.453558e-04
## avcondition 0.000000e+00 0.000000e+00 -2.610917e-03
## T_av 0.000000e+00 0.000000e+00 -1.135202e-05
## O2_sat_av 0.000000e+00 0.000000e+00 -1.893466e-05
## Con_av 0.000000e+00 4.241640e-04 6.570599e-05
## COD_av 0.000000e+00 0.000000e+00 9.451636e-05
## NH4._av 0.000000e+00 1.819425e-02 1.713706e-03
## Nt_av 0.000000e+00 0.000000e+00 1.739937e-04
## pool_riffle1 0.000000e+00 0.000000e+00 2.104077e-04
## meander1 0.000000e+00 0.000000e+00 -6.652944e-04
## netcen -3.849846e-06 3.938057e-08 -3.317353e-07
## updist -1.358072e-06 0.000000e+00 -1.309710e-07
## attr(,"Probability")
## [1] 0.95
## attr(,"class")
## [1] "confint.bas"plot(confint(coef.model, parm = 2:11))
## NULLconfint <- confint(coef.model, parm = 2:11)
write.table(confint, 'GyroAA.txt', sep="\t")
pip <- summary(bas.model)
PIP[c(1:12),3] <- pip[2:13,1]*sign(coef.model$postmean[2:13])6.5 Variation in Glugea infection
bas.model <- bas.glm(pa$Glu ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av +
NH4._av + Nt_av + SM_av + pool_riffle + meander +
spavar$netcen + spavar$updist, data=environment2, betaprior=g.prior(100), family=binomial)
yhat = fitted(bas.model, estimator = "BMA") #these are the fitted values under BMA
r = bas.model$Y - yhat #these are the model residuals
plot(bas.model)
summary(bas.model)## P(B != 0 | Y) model 1 model 2 model 3 model 4
## Intercept 1.0000000 1.000000 1.000000 1.0000000 1.0000000
## avlength 0.6612793 0.000000 0.000000 1.0000000 0.0000000
## avcondition 0.4488281 0.000000 1.000000 1.0000000 1.0000000
## T_av 0.2731201 0.000000 0.000000 0.0000000 0.0000000
## O2_sat_av 0.3873901 0.000000 0.000000 0.0000000 0.0000000
## Con_av 0.9977539 1.000000 1.000000 1.0000000 1.0000000
## COD_av 0.5381714 1.000000 0.000000 1.0000000 1.0000000
## NH4._av 0.8386841 1.000000 1.000000 1.0000000 1.0000000
## Nt_av 0.5621582 1.000000 1.000000 0.0000000 1.0000000
## SM_av 0.4463501 1.000000 1.000000 0.0000000 0.0000000
## pool_riffle1 0.4196533 0.000000 0.000000 0.0000000 0.0000000
## meander1 0.9978149 1.000000 1.000000 1.0000000 1.0000000
## spavar$netcen 0.4684082 0.000000 1.000000 0.0000000 0.0000000
## spavar$updist 0.8170898 1.000000 0.000000 1.0000000 1.0000000
## BF NA 1.000000 0.769804 0.8189871 0.7351301
## PostProbs NA 0.048200 0.047300 0.0455000 0.0346000
## R2 NA 1.000000 1.000000 1.0000000 1.0000000
## dim NA 8.000000 8.000000 8.0000000 8.0000000
## logmarg NA -5.808403 -6.070023 -6.0080902 -6.1161110
## model 5
## Intercept 1.0000000
## avlength 1.0000000
## avcondition 0.0000000
## T_av 0.0000000
## O2_sat_av 1.0000000
## Con_av 1.0000000
## COD_av 0.0000000
## NH4._av 0.0000000
## Nt_av 0.0000000
## SM_av 0.0000000
## pool_riffle1 1.0000000
## meander1 1.0000000
## spavar$netcen 1.0000000
## spavar$updist 1.0000000
## BF 0.5830791
## PostProbs 0.0299000
## R2 1.0000000
## dim 8.0000000
## logmarg -6.3478357image(bas.model, rotate=F)
coef.model <- coef(bas.model)## Warning in object$prior == "AIC" || object$prior == "BIC": 'length(x) = 569 > 1'
## in coercion to 'logical(1)'
## Warning in object$prior == "AIC" || object$prior == "BIC": 'length(x) = 569 > 1'
## in coercion to 'logical(1)'abs(coef.model$postmean)-2*coef.model$postsd > 0## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSEconfint(coef.model)## 2.5% 97.5% beta
## Intercept -2.825792e+06 2.710683e+06 -6.369350e+03
## avlength -4.309607e+04 3.935300e+04 9.028577e+01
## avcondition -9.449562e+05 9.095393e+05 -7.711752e+00
## T_av -2.742780e+04 3.022164e+04 1.728316e+01
## O2_sat_av -5.333463e+03 5.629149e+03 8.996676e+00
## Con_av -1.563938e+03 1.596557e+03 4.398577e+00
## COD_av -1.383043e+04 1.491038e+04 9.484729e+00
## NH4._av -2.206263e+05 2.216950e+05 -2.719259e+02
## Nt_av -4.701129e+04 4.562539e+04 3.847862e+01
## SM_av -2.409398e+03 2.246281e+03 -1.183195e+00
## pool_riffle1 -1.415306e+05 1.540517e+05 8.128272e+01
## meander1 -5.313945e+05 4.746843e+05 -1.369205e+03
## spavar$netcen -1.073751e+01 9.266554e+00 -8.796714e-03
## spavar$updist -5.000750e+00 4.887079e+00 -8.188871e-03
## attr(,"Probability")
## [1] 0.95
## attr(,"class")
## [1] "confint.bas"plot(confint(coef.model, parm = 2:11))
## NULLconfint <- confint(coef.model, parm = 2:11)
pip <- summary(bas.model)
PIP[c(1:12),9] <- pip[2:13,1]*sign(coef.model$postmean[2:13])6.5 Variation in Contracaecum infection
bas.model <- bas.glm(pa$Con ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av +
NH4._av + Nt_av + SM_av + pool_riffle + meander +
spavar$netcen + spavar$updist, data=environment2, betaprior=g.prior(100), family=binomial)
yhat = fitted(bas.model, estimator = "BMA") #these are the fitted values under BMA
r = bas.model$Y - yhat #these are the model residuals
plot(bas.model)
summary(bas.model)## P(B != 0 | Y) model 1 model 2 model 3 model 4
## Intercept 1.00000000 1.00000 1.0000000 1.0000000 1.0000000
## avlength 0.07762451 0.00000 1.0000000 0.0000000 0.0000000
## avcondition 0.02630615 0.00000 0.0000000 1.0000000 0.0000000
## T_av 0.01102295 0.00000 0.0000000 0.0000000 0.0000000
## O2_sat_av 0.02724609 0.00000 0.0000000 0.0000000 1.0000000
## Con_av 0.01230469 0.00000 0.0000000 0.0000000 0.0000000
## COD_av 0.01250000 0.00000 0.0000000 0.0000000 0.0000000
## NH4._av 0.01958008 0.00000 0.0000000 0.0000000 0.0000000
## Nt_av 0.01236572 0.00000 0.0000000 0.0000000 0.0000000
## SM_av 0.01303711 0.00000 0.0000000 0.0000000 0.0000000
## pool_riffle1 0.01182861 0.00000 0.0000000 0.0000000 0.0000000
## meander1 0.01090088 0.00000 0.0000000 0.0000000 0.0000000
## spavar$netcen 0.01875000 0.00000 0.0000000 0.0000000 0.0000000
## spavar$updist 0.01165771 0.00000 0.0000000 0.0000000 0.0000000
## BF NA 1.00000 0.9507459 0.2581488 0.2583995
## PostProbs NA 0.79370 0.0548000 0.0152000 0.0150000
## R2 NA 0.00000 0.0864000 0.0365000 0.0366000
## dim NA 1.00000 2.0000000 2.0000000 2.0000000
## logmarg NA -25.82594 -25.8764468 -27.1801577 -27.1791867
## model 5
## Intercept 1.00000
## avlength 0.00000
## avcondition 0.00000
## T_av 0.00000
## O2_sat_av 0.00000
## Con_av 0.00000
## COD_av 0.00000
## NH4._av 1.00000
## Nt_av 0.00000
## SM_av 0.00000
## pool_riffle1 0.00000
## meander1 0.00000
## spavar$netcen 0.00000
## spavar$updist 0.00000
## BF 0.19791
## PostProbs 0.01260
## R2 0.02640
## dim 2.00000
## logmarg -27.44588image(bas.model, rotate=F)
coef.model <- coef(bas.model)## Warning in object$prior == "AIC" || object$prior == "BIC": 'length(x) = 354 > 1'
## in coercion to 'logical(1)'
## Warning in object$prior == "AIC" || object$prior == "BIC": 'length(x) = 354 > 1'
## in coercion to 'logical(1)'abs(coef.model$postmean)-2*coef.model$postsd > 0## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSEconfint(coef.model)## 2.5% 97.5% beta
## Intercept -8.822922 2.2340459 -5.200393e-01
## avlength 0.000000 0.1521401 1.443680e-02
## avcondition 0.000000 0.0000000 -1.725841e-01
## T_av 0.000000 0.0000000 8.731866e-04
## O2_sat_av 0.000000 0.0000000 1.187055e-03
## Con_av 0.000000 0.0000000 7.077447e-06
## COD_av 0.000000 0.0000000 -3.261292e-04
## NH4._av 0.000000 0.0000000 -5.935914e-03
## Nt_av 0.000000 0.0000000 -9.420205e-04
## SM_av 0.000000 0.0000000 1.258097e-04
## pool_riffle1 0.000000 0.0000000 -5.262390e-03
## meander1 0.000000 0.0000000 3.109108e-03
## spavar$netcen 0.000000 0.0000000 -8.628183e-07
## spavar$updist 0.000000 0.0000000 -1.107421e-07
## attr(,"Probability")
## [1] 0.95
## attr(,"class")
## [1] "confint.bas"plot(confint(coef.model, parm = 2:11))
## NULLconfint <- confint(coef.model, parm = 2:11)
pip <- summary(bas.model)
PIP[c(1:12),10] <- pip[2:13,1]*sign(coef.model$postmean[2:13])Variation in Anguillicoloides infection
bas.model <- bas.glm(pa$Ang ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av +
NH4._av + Nt_av + SM_av + pool_riffle + meander +
spavar$netcen + spavar$updist, data=environment2, betaprior=g.prior(100), family=binomial)
yhat = fitted(bas.model, estimator = "BMA") #these are the fitted values under BMA
r = bas.model$Y - yhat #these are the model residuals
plot(bas.model)
summary(bas.model)## P(B != 0 | Y) model 1 model 2 model 3 model 4
## Intercept 1.0000000 1.000000 1.0000000 1.0000000 1.0000000
## avlength 0.9590210 1.000000 1.0000000 1.0000000 1.0000000
## avcondition 0.3462524 0.000000 0.0000000 0.0000000 0.0000000
## T_av 0.9616455 1.000000 1.0000000 1.0000000 1.0000000
## O2_sat_av 0.8349487 1.000000 1.0000000 1.0000000 1.0000000
## Con_av 0.5795410 0.000000 1.0000000 1.0000000 0.0000000
## COD_av 0.3835938 0.000000 0.0000000 1.0000000 0.0000000
## NH4._av 0.9710571 1.000000 1.0000000 1.0000000 1.0000000
## Nt_av 0.5616821 0.000000 1.0000000 0.0000000 0.0000000
## SM_av 0.9464844 1.000000 1.0000000 1.0000000 1.0000000
## pool_riffle1 0.3850952 0.000000 0.0000000 0.0000000 1.0000000
## meander1 0.9624023 1.000000 1.0000000 1.0000000 1.0000000
## spavar$netcen 0.8757202 1.000000 1.0000000 1.0000000 1.0000000
## spavar$updist 0.5311157 1.000000 0.0000000 0.0000000 1.0000000
## BF NA 1.000000 0.4728289 0.3794633 0.2858158
## PostProbs NA 0.075900 0.0701000 0.0502000 0.0420000
## R2 NA 1.000000 1.0000000 1.0000000 1.0000000
## dim NA 9.000000 10.0000000 10.0000000 10.0000000
## logmarg NA -9.802499 -10.5515211 -10.7714968 -11.0549070
## model 5
## Intercept 1.0000000
## avlength 1.0000000
## avcondition 1.0000000
## T_av 1.0000000
## O2_sat_av 0.0000000
## Con_av 1.0000000
## COD_av 0.0000000
## NH4._av 1.0000000
## Nt_av 1.0000000
## SM_av 1.0000000
## pool_riffle1 0.0000000
## meander1 1.0000000
## spavar$netcen 1.0000000
## spavar$updist 0.0000000
## BF 0.2037316
## PostProbs 0.0320000
## R2 1.0000000
## dim 10.0000000
## logmarg -11.3934513image(bas.model, rotate=F)
coef.model <- coef(bas.model)## Warning in object$prior == "AIC" || object$prior == "BIC": 'length(x) = 330 > 1'
## in coercion to 'logical(1)'
## Warning in object$prior == "AIC" || object$prior == "BIC": 'length(x) = 330 > 1'
## in coercion to 'logical(1)'abs(coef.model$postmean)-2*coef.model$postsd > 0## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSEconfint(coef.model)## 2.5% 97.5% beta
## Intercept -2.386597e+06 2.273643e+06 -6.535648e+03
## avlength -4.586713e+04 4.408703e+04 1.346662e+02
## avcondition -7.819640e+05 8.464993e+05 -3.655426e+02
## T_av -1.630930e+05 1.565266e+05 6.012930e+02
## O2_sat_av -1.997370e+04 1.776730e+04 -3.845302e+01
## Con_av -6.764710e+02 6.776503e+02 3.950266e-01
## COD_av -7.640585e+03 7.575599e+03 1.799636e+00
## NH4._av -3.616578e+05 3.231969e+05 -1.075229e+03
## Nt_av -4.446612e+04 4.557655e+04 -4.776944e+01
## SM_av -5.506008e+03 6.217337e+03 1.312965e+01
## pool_riffle1 -1.379536e+05 1.324286e+05 -1.304974e+02
## meander1 -3.986089e+05 4.061205e+05 -1.417065e+03
## spavar$netcen -1.409875e+01 1.503926e+01 -2.813438e-02
## spavar$updist -3.898065e+00 2.878999e+00 1.216461e-03
## attr(,"Probability")
## [1] 0.95
## attr(,"class")
## [1] "confint.bas"plot(confint(coef.model, parm = 2:11))
## NULLconfint <- confint(coef.model, parm = 2:11)
pip <- summary(bas.model)
PIP[c(1:12),11] <- pip[2:13,1]*sign(coef.model$postmean[2:13])Variation in Individual Parasitation Index (all parasites)
bas.model <- bas.lm(avPI ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
yhat = fitted(bas.model, estimator = "BMA") #these are the fitted values under BMA
r = bas.model$Y - yhat #these are the model residuals
plot(bas.model)
summary(bas.model)## P(B != 0 | Y) model 1 model 2 model 3 model 4 model 5
## Intercept 1.0000000 1.00000000 1.00000000 1.00000000 1.000000 1.000000
## avlength 0.2261960 0.00000000 0.00000000 0.00000000 0.000000 0.000000
## avcondition 0.1810890 0.00000000 0.00000000 0.00000000 0.000000 0.000000
## T_av 0.4574073 0.00000000 1.00000000 0.00000000 1.000000 0.000000
## O2_sat_av 0.1952767 0.00000000 0.00000000 0.00000000 0.000000 0.000000
## Con_av 0.4598919 0.00000000 0.00000000 0.00000000 0.000000 1.000000
## COD_av 0.5684747 0.00000000 0.00000000 0.00000000 0.000000 1.000000
## NH4._av 0.2748144 0.00000000 0.00000000 0.00000000 0.000000 0.000000
## Nt_av 0.4582802 0.00000000 0.00000000 1.00000000 1.000000 0.000000
## pool_riffle1 0.3054914 0.00000000 0.00000000 0.00000000 0.000000 0.000000
## meander1 0.1985617 0.00000000 0.00000000 0.00000000 0.000000 0.000000
## netcen 0.1910872 0.00000000 0.00000000 0.00000000 0.000000 0.000000
## updist 0.4153657 0.00000000 0.00000000 0.00000000 0.000000 1.000000
## BF NA 0.01969453 0.09251289 0.08737546 0.355023 1.000000
## PostProbs NA 0.10850000 0.04250000 0.04010000 0.029600 0.025000
## R2 NA 0.00000000 0.18980000 0.18710000 0.325400 0.424800
## dim NA 1.00000000 2.00000000 2.00000000 3.000000 4.000000
## logmarg NA 0.00000000 1.54700725 1.48987379 2.891842 3.927415image(bas.model, rotate=F)
coef.model <- coef(bas.model)
abs(coef.model$postmean)-2*coef.model$postsd > 0## [1] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSEconfint(coef.model)## 2.5% 97.5% beta
## Intercept 1.118420e+00 1.711581e+00 1.422090e+00
## avlength -8.794753e-02 1.762219e-02 -7.992245e-03
## avcondition -3.185059e+00 1.403626e+00 -9.325811e-02
## T_av -3.274153e-03 3.715347e-01 8.644408e-02
## O2_sat_av -2.698197e-02 1.308221e-02 -7.476618e-04
## Con_av -5.439853e-06 3.163554e-03 8.405554e-04
## COD_av 0.000000e+00 6.150099e-02 2.109188e-02
## NH4._av -5.045011e-01 1.915575e-01 -4.965441e-02
## Nt_av 0.000000e+00 2.286946e-01 5.507750e-02
## pool_riffle1 -5.094306e-02 8.726103e-01 1.389140e-01
## meander1 -6.379291e-01 2.037381e-01 -3.866036e-02
## netcen -2.803839e-05 2.205411e-05 -1.270941e-06
## updist -2.679058e-05 5.689670e-09 -5.952452e-06
## attr(,"Probability")
## [1] 0.95
## attr(,"class")
## [1] "confint.bas"plot(confint(coef.model, parm = 2:11))
## NULLconfint <- confint(coef.model, parm = 2:11)
pip <- summary(bas.model)
PIP[c(1:12),12] <- pip[2:13,1]*sign(coef.model$postmean[2:13])Prediction plot for marginal effect of COD on Individual Parasitation Index
newdata1 <- newdata; newdata1[,"COD_av"] <- environment2$COD_av
BMA <- predict(bas.model, newdata = newdata1, estimator = "BMA", se.fit=TRUE)
ggplot(environment2, aes(COD_av, BMA$fit)) +
theme_bw() +
geom_line(color="red", size=1) +
geom_ribbon(aes(ymin = (BMA$fit-BMA$se.bma.fit), ymax = (BMA$fit+BMA$se.bma.fit)), alpha = .1) +
geom_point(data = environment2, aes(x=COD_av, y=avPI)) +
labs(x=expression("COD"), y=expression("Individual Parasitation Index")) +
theme(axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12)) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
Variation in Individual Parasitation Index (only ectoparasites)
bas.model <- bas.lm(avPI_ecto ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
yhat = fitted(bas.model, estimator = "BMA") #these are the fitted values under BMA
r = bas.model$Y - yhat #these are the model residuals
plot(bas.model)
summary(bas.model)## P(B != 0 | Y) model 1 model 2 model 3 model 4 model 5
## Intercept 1.0000000 1.0000000 1.000000000 1.0000000 1.000000 1.000000
## avlength 0.4669536 0.0000000 1.000000000 0.0000000 0.000000 1.000000
## avcondition 0.3758074 0.0000000 1.000000000 0.0000000 0.000000 0.000000
## T_av 0.2753561 0.0000000 1.000000000 0.0000000 0.000000 0.000000
## O2_sat_av 0.2919614 0.0000000 1.000000000 0.0000000 0.000000 0.000000
## Con_av 0.6617062 0.0000000 1.000000000 1.0000000 0.000000 0.000000
## COD_av 0.8437205 0.0000000 1.000000000 1.0000000 1.000000 1.000000
## NH4._av 0.4163705 0.0000000 1.000000000 0.0000000 0.000000 0.000000
## Nt_av 0.8302866 1.0000000 1.000000000 0.0000000 1.000000 1.000000
## pool_riffle1 0.4025108 0.0000000 1.000000000 0.0000000 0.000000 0.000000
## meander1 0.5031625 0.0000000 1.000000000 0.0000000 0.000000 0.000000
## netcen 0.3010717 0.0000000 1.000000000 0.0000000 0.000000 0.000000
## updist 0.3285744 0.0000000 1.000000000 0.0000000 0.000000 0.000000
## BF NA 0.1318791 0.008313608 0.4282678 0.319205 1.000000
## PostProbs NA 0.0510000 0.038600000 0.0301000 0.022500 0.021100
## R2 NA 0.3007000 0.682800000 0.4141000 0.403700 0.496600
## dim NA 2.0000000 13.000000000 3.0000000 3.000000 4.000000
## logmarg NA 4.0635523 1.299560343 5.2414154 4.947500 6.089422image(bas.model, rotate=F)
coef.model <- coef(bas.model)
abs(coef.model$postmean)-2*coef.model$postsd > 0## [1] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSEconfint(coef.model)## 2.5% 97.5% beta
## Intercept 6.758354e-01 1.145843e+00 9.199577e-01
## avlength -1.074205e-01 7.195116e-03 -2.357999e-02
## avcondition -4.448220e+00 5.860600e-01 -7.055549e-01
## T_av -9.391074e-02 1.376909e-01 5.818763e-03
## O2_sat_av -1.425971e-02 2.512049e-02 1.122707e-03
## Con_av 0.000000e+00 3.201604e-03 1.203349e-03
## COD_av 0.000000e+00 6.383947e-02 3.405818e-02
## NH4._av -4.856138e-01 7.930413e-02 -8.034086e-02
## Nt_av 0.000000e+00 2.520153e-01 1.257830e-01
## pool_riffle1 -5.647613e-02 8.378715e-01 1.490513e-01
## meander1 -9.300167e-01 0.000000e+00 -2.358742e-01
## netcen -3.333049e-05 1.969508e-05 -2.447510e-06
## updist -1.557366e-05 3.486981e-06 -1.836166e-06
## attr(,"Probability")
## [1] 0.95
## attr(,"class")
## [1] "confint.bas"plot(confint(coef.model, parm = 2:11))
## NULLconfint <- confint(coef.model, parm = 2:11)
pip <- summary(bas.model)
PIP[c(1:12),13] <- pip[2:13,1]*sign(coef.model$postmean[2:13])Prediction plot for marginal effect of COD on Individual Parasitation Index (only ectoparasites)
newdata1 <- newdata; newdata1[,"COD_av"] <- environment2$COD_av
BMA <- predict(bas.model, newdata = newdata1, estimator = "BMA", se.fit=TRUE)
ggplot(environment2, aes(COD_av, BMA$fit)) +
theme_bw() +
geom_line(color="red", size=1) +
geom_ribbon(aes(ymin = (BMA$fit-BMA$se.bma.fit), ymax = (BMA$fit+BMA$se.bma.fit)), alpha = .1) +
geom_point(data = environment2, aes(x=COD_av, y=avPI_ecto)) +
labs(x=expression("COD"), y=expression("Individual Parasitation Index (only ectoparasites)")) +
theme(axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12)) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
Prediction plot for marginal effect of total nitrogen on Individual Parasitation Index (only ectoparasites)
newdata1 <- newdata; newdata1[,"Nt_av"] <- environment2$Nt_av
BMA <- predict(bas.model, newdata = newdata1, estimator = "BMA", se.fit=TRUE)
ggplot(environment2, aes(Nt_av, BMA$fit)) +
theme_bw() +
geom_line(color="red", size=1) +
geom_ribbon(aes(ymin = (BMA$fit-BMA$se.bma.fit), ymax = (BMA$fit+BMA$se.bma.fit)), alpha = .1) +
geom_point(data = environment2, aes(x=Nt_av, y=avPI_ecto)) +
labs(x=expression("Total nitrogen"), y=expression("Individual Parasitation Index (only ectoparasites)")) +
theme(axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12)) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
Prediction plot for marginal effect of conductivity on Individual Parasitation Index (only ectoparasites)
newdata1 <- newdata; newdata1[,"Con_av"] <- environment2$Con_av
BMA <- predict(bas.model, newdata = newdata1, estimator = "BMA", se.fit=TRUE)
ggplot(environment2, aes(Con_av, BMA$fit)) +
theme_bw() +
geom_line(color="red", size=1) +
geom_ribbon(aes(ymin = (BMA$fit-BMA$se.bma.fit), ymax = (BMA$fit+BMA$se.bma.fit)), alpha = .1) +
geom_point(data = environment2, aes(x=Con_av, y=avPI)) +
labs(x=expression("Conductivity"), y=expression("Individual Parasitation Index (only ectoparasites)")) +
theme(axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12)) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
Prediction plot for marginal effect of meanders on Individual Parasitation Index (only ectoparasites)
newdata1 <- newdata; newdata1[,"meander"] <- environment2$meander
BMA <- predict(bas.model, newdata = newdata1, estimator = "BMA", se.fit=TRUE)
ggplot(environment2, aes(meander, BMA$fit)) +
theme_bw() +
#geom_line(color="red", size=1) +
#geom_ribbon(aes(ymin = (BMA$fit-BMA$se.bma.fit), ymax = (BMA$fit+BMA$se.bma.fit)), alpha = .1) +
geom_point(data = environment2, aes(x=meander, y=avPI)) +
geom_boxplot(aes(lower = (BMA$fit-BMA$se.bma.fit), middle = BMA$fit, upper = (BMA$fit+BMA$se.bma.fit))) +
labs(x=expression("Meander"), y=expression("Individual Parasitation Index (only ectoparasites)")) +
theme(axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12)) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
Variation in Individual Parasitation Index (only endoparasites)
bas.model <- bas.lm(avPI_endo ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2, prior="JZS")
yhat = fitted(bas.model, estimator = "BMA") #these are the fitted values under BMA
r = bas.model$Y - yhat #these are the model residuals
plot(bas.model)
summary(bas.model)## P(B != 0 | Y) model 1 model 2 model 3 model 4 model 5
## Intercept 1.00000000 1.0000000 1.000000 1.00000000 1.0000000 1.0000000
## avlength 0.06442140 0.0000000 0.000000 0.00000000 0.0000000 0.0000000
## avcondition 0.12246410 0.0000000 0.000000 1.00000000 0.0000000 0.0000000
## T_av 0.30397004 0.0000000 1.000000 0.00000000 0.0000000 1.0000000
## O2_sat_av 0.05178919 0.0000000 0.000000 0.00000000 0.0000000 0.0000000
## Con_av 0.05158494 0.0000000 0.000000 0.00000000 0.0000000 0.0000000
## COD_av 0.04593088 0.0000000 0.000000 0.00000000 0.0000000 0.0000000
## NH4._av 0.08528364 0.0000000 0.000000 0.00000000 0.0000000 1.0000000
## Nt_av 0.06477759 0.0000000 0.000000 0.00000000 0.0000000 0.0000000
## pool_riffle1 0.04504579 0.0000000 0.000000 0.00000000 0.0000000 0.0000000
## meander1 0.05899866 0.0000000 0.000000 0.00000000 0.0000000 0.0000000
## netcen 0.05414430 0.0000000 0.000000 0.00000000 0.0000000 0.0000000
## updist 0.10936917 0.0000000 0.000000 0.00000000 1.0000000 0.0000000
## BF NA 0.3484238 1.000000 0.33059983 0.2877395 0.6134470
## PostProbs NA 0.4921000 0.117700 0.03890000 0.0339000 0.0131000
## R2 NA 0.0000000 0.166100 0.11040000 0.1032000 0.2244000
## dim NA 1.0000000 2.000000 2.00000000 2.0000000 3.0000000
## logmarg NA 0.0000000 1.054336 -0.05251095 -0.1913639 0.5656742image(bas.model, rotate=F)
coef.model <- coef(bas.model)
abs(coef.model$postmean)-2*coef.model$postsd > 0## [1] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSEconfint(coef.model)## 2.5% 97.5% beta
## Intercept 3.151330e-01 7.021383e-01 4.947994e-01
## avlength -1.412832e-03 1.080428e-02 1.398963e-03
## avcondition -1.620055e-03 2.632662e+00 2.446911e-01
## T_av 0.000000e+00 2.126705e-01 4.520999e-02
## O2_sat_av -2.267240e-04 1.121255e-06 -2.446396e-04
## Con_av 0.000000e+00 0.000000e+00 -1.385311e-05
## COD_av 0.000000e+00 0.000000e+00 -1.072193e-04
## NH4._av -1.100309e-01 0.000000e+00 -9.322620e-03
## Nt_av -2.432447e-02 0.000000e+00 -2.149910e-03
## pool_riffle1 0.000000e+00 0.000000e+00 2.267863e-03
## meander1 0.000000e+00 8.897285e-02 1.005422e-02
## netcen -2.237261e-06 1.741707e-07 -3.607730e-07
## updist -8.617171e-06 0.000000e+00 -8.091451e-07
## attr(,"Probability")
## [1] 0.95
## attr(,"class")
## [1] "confint.bas"plot(confint(coef.model, parm = 2:11))## Warning in arrows(x[not.deg], ci[not.deg, 1], x[not.deg], ci[not.deg, 2], :
## zero-length arrow is of indeterminate angle and so skipped
## NULLconfint <- confint(coef.model, parm = 2:11)
pip <- summary(bas.model)
PIP[c(1:12),14] <- pip[2:13,1]*sign(coef.model$postmean[2:13])7. BORAL analysis
Model-based analysis of multivariate abundance data using Bayesian Markov chain Monte Carlo methods for parameter estimation
7.1 BORAL analysis for average abundances of parasites
library(boral)## Loading required package: coda## This is boral version 2.0. If you recently updated boral, please check news(package = "boral") for the updates in the latest version.data$Site <- as.factor(data$site)
levels(data$site) <- levels(as.factor(environment2$Site))
data_m <- merge(data, environment2, by = "Site")
data_all <- na.omit(data_m)
names(data_all)## [1] "Site" "site" "fish"
## [4] "parspeciesrichness" "div_shannon" "div_simpson"
## [7] "temp" "pH" "conductivity"
## [10] "nitrogen" "phosphorus" "oxygen"
## [13] "netcen.x" "updist.x" "updist2"
## [16] "updist3" "fishspeciesrichness" "weight"
## [19] "weigh..g." "length" "SMI"
## [22] "Sex" "Gyr" "Tri"
## [25] "Glu" "ecto_screener" "Con"
## [28] "CysL" "Pro" "Aca"
## [31] "Cam" "Ang" "CysI"
## [34] "endo_screener" "PI" "PI_ecto"
## [37] "PI_endo" "T_av" "O2_sat_av"
## [40] "Con_av" "COD_av" "NH4._av"
## [43] "Nt_av" "SM_av" "pool_riffle"
## [46] "meander" "updist.y" "netcen.y"avcondition <- aggregate(data$SMI, by = list(data[,1]), function(x){mean(x, na.rm =T)})[,2]
avlength <- aggregate(data$length, by = list(data[,1]), function(x){mean(x, na.rm =T)})[,2]
y <- round(cbind(avab$Gyr, avab$Tri, avab$Glu, avab$Con, avab$Ang))
X <- cbind(avcondition,
avlength,
environment2$T_av,
environment2$O2_sat_av,
environment2$Con_av,
environment2$COD_av,
environment2$NH4._av,
environment2$Nt_av,
environment2$netcen,
environment2$updist,
as.numeric(environment2$pool_riffle),
as.numeric(environment2$meander))
colnames(X) <- c("avcondition", "avlength", "T", "O2", "Con", "COD", "NH4", "Nt", "netcen", "updist", "pool_riffle", "meander")
example_mcmc_control <- list(n.burnin = 1000, n.iteration = 10000, n.thin = 1)
testpath <- file.path(tempdir(), "jagsboralmodel.txt")
paramod <- boral(y, X = X,
family = "negative.binomial",
mcmc.control = example_mcmc_control,
model.name = testpath,
lv.control = list(num.lv = 2, type = "independent"),
save.model = TRUE)## module glm loaded## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 185
## Unobserved stochastic nodes: 338
## Total graph size: 2173
##
## Initializing modelplot(paramod)## NULL
coefsplot(covname = "avcondition", object = paramod) #Condition
coefsplot(covname = "avlength", object = paramod) #Length
coefsplot(covname = "T", object = paramod) #Temperature
coefsplot(covname = "O2", object = paramod) #Oxygen
coefsplot(covname = "Con", object = paramod) #Conductivity
coefsplot(covname = "COD", object = paramod) #COD
coefsplot(covname = "NH4", object = paramod) #NH4
coefsplot(covname = "Nt", object = paramod) #Nt
coefsplot(covname = "netcen", object = paramod) #netcen
coefsplot(covname = "updist", object = paramod) #updist
coefsplot(covname = "pool_riffle", object = paramod) #poolriffle
coefsplot(covname = "meander", object = paramod) #meander
envcors <- get.enviro.cor(paramod)
rescors <- get.residual.cor(paramod)
library(corrplot)## corrplot 0.92 loadedcorrplot(envcors$sig.cor, type = "lower", diag = FALSE, title = "Correlations due to covariates", mar = c(3,0.5,2,1), tl.srt = 45)
corrplot(rescors$sig.cor, type = "lower", diag = FALSE, title = "Residual correlations", mar = c(3,0.5,2,1), tl.srt = 45)
7.1 BORAL analysis for median infection intensities of parasites
y <- round(cbind(medin$Gyr, medin$Tri, medin$Glu, medin$Con, medin$Ang))
paramod <- boral(y, X = X,
family = "negative.binomial",
mcmc.control = example_mcmc_control,
model.name = testpath,
lv.control = list(num.lv = 2, type = "independent"),
save.model = TRUE)## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 185
## Unobserved stochastic nodes: 338
## Total graph size: 2173
##
## Initializing modelplot(paramod)## NULL
coefsplot(covname = "avcondition", object = paramod) #Condition
coefsplot(covname = "avlength", object = paramod) #Length
coefsplot(covname = "T", object = paramod) #Temperature
coefsplot(covname = "O2", object = paramod) #Oxygen
coefsplot(covname = "Con", object = paramod) #Conductivity
coefsplot(covname = "COD", object = paramod) #COD
coefsplot(covname = "NH4", object = paramod) #NH4
coefsplot(covname = "Nt", object = paramod) #Nt
coefsplot(covname = "netcen", object = paramod) #netcen
coefsplot(covname = "updist", object = paramod) #updist
coefsplot(covname = "pool_riffle", object = paramod) #poolriffle
coefsplot(covname = "meander", object = paramod) #meander
envcors <- get.enviro.cor(paramod)
rescors <- get.residual.cor(paramod)
library(corrplot)
corrplot(envcors$sig.cor, type = "lower", diag = FALSE, title = "Correlations due to covariates", mar = c(3,0.5,2,1), tl.srt = 45)
corrplot(rescors$sig.cor, type = "lower", diag = FALSE, title = "Residual correlations", mar = c(3,0.5,2,1), tl.srt = 45)
8. Multivariate analysis
8.1 Component communities
# Component communities: Bray-Curtis dissimilarities based on Hellinger transformed average abundance data
spe.hel_bray <- vegdist(decostand(avab[,-1], na.rm=T, method="hellinger"), method="bray", na.rm=T)
# Check whether Euclidean and Bray-Curtis distances are comparable
spe.hel_euc <- vegdist(decostand(avab[,-1], na.rm=T, method="hellinger"), method="euc", na.rm=T)
plot(spe.hel_bray, spe.hel_euc)
mantel(spe.hel_bray, spe.hel_euc)##
## Mantel statistic based on Pearson's product-moment correlation
##
## Call:
## mantel(xdis = spe.hel_bray, ydis = spe.hel_euc)
##
## Mantel statistic r: 0.9648
## Significance: 0.001
##
## Upper quantiles of permutations (null model):
## 90% 95% 97.5% 99%
## 0.110 0.146 0.177 0.198
## Permutation: free
## Number of permutations: 999adonis(spe.hel_bray ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2)## 'adonis' will be deprecated: use 'adonis2' instead## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## avlength 1 0.1863 0.18629 1.8398 0.04303 0.118
## avcondition 1 0.1305 0.13055 1.2893 0.03016 0.309
## T_av 1 0.3204 0.32039 3.1643 0.07401 0.014 *
## O2_sat_av 1 0.1368 0.13678 1.3509 0.03160 0.259
## Con_av 1 0.1326 0.13262 1.3098 0.03064 0.283
## COD_av 1 0.0657 0.06568 0.6486 0.01517 0.641
## NH4._av 1 0.2040 0.20399 2.0146 0.04712 0.098 .
## Nt_av 1 0.1451 0.14509 1.4329 0.03352 0.224
## pool_riffle 1 0.0853 0.08529 0.8424 0.01970 0.544
## meander 1 0.2029 0.20286 2.0035 0.04686 0.092 .
## netcen 1 0.2173 0.21728 2.1459 0.05019 0.074 .
## updist 1 0.0719 0.07193 0.7104 0.01662 0.597
## Residuals 24 2.4301 0.10125 0.56137
## Total 36 4.3288 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $call
## adonis(formula = spe.hel_bray ~ avlength + avcondition + T_av +
## O2_sat_av + Con_av + COD_av + NH4._av + Nt_av + pool_riffle +
## meander + netcen + updist, data = environment2)
##
## $coefficients
## NULL
##
## $coef.sites
## [,1] [,2] [,3] [,4]
## (Intercept) 5.508408e-01 -5.273740e-02 -2.434806e-01 9.195991e-02
## avlength -6.730327e-03 -5.154478e-03 -3.437304e-03 3.017426e-03
## avcondition 3.533446e-01 6.299677e-01 6.705073e-01 -1.509499e-01
## T_av 1.118039e-02 -1.547332e-02 2.284308e-02 3.609975e-02
## O2_sat_av -1.952659e-03 3.216838e-03 -2.736684e-03 -3.275301e-03
## Con_av -5.105173e-04 -7.421868e-05 -2.766052e-04 -2.644205e-04
## COD_av -2.877120e-03 2.012742e-03 -5.828066e-04 2.055569e-03
## NH4._av 2.840428e-02 2.361253e-02 -5.333225e-02 -1.083596e-01
## Nt_av -1.398084e-02 3.352012e-03 1.765257e-02 1.505213e-02
## pool_riffle1 1.768819e-02 -5.367793e-04 3.673375e-03 2.606624e-02
## meander1 -7.321195e-02 -1.737680e-02 3.462634e-02 -6.906184e-03
## netcen 5.687407e-06 3.122369e-06 6.705984e-06 7.354323e-06
## updist -7.990288e-07 -3.330355e-07 1.097578e-07 -1.418553e-06
## [,5] [,6] [,7] [,8]
## (Intercept) 3.417559e-01 6.599748e-01 -6.656732e-01 1.232299e+00
## avlength -5.220827e-03 -8.875861e-03 1.624719e-02 -8.270956e-03
## avcondition 7.063362e-01 5.898287e-01 -9.628888e-02 1.786671e-01
## T_av 3.555749e-03 -2.036964e-02 7.082503e-02 -1.630523e-02
## O2_sat_av -3.559094e-03 -3.677428e-03 -1.366724e-03 -3.240107e-03
## Con_av -2.630009e-04 -3.274615e-04 1.607746e-04 -2.325954e-04
## COD_av -1.859208e-03 -1.344927e-03 -2.168911e-03 -1.552806e-03
## NH4._av -1.230503e-02 -6.366620e-03 -2.906617e-02 3.139836e-02
## Nt_av 3.624518e-03 6.253769e-03 -2.142983e-02 2.965146e-04
## pool_riffle1 -1.857388e-02 -1.937103e-02 1.767055e-02 -2.156985e-02
## meander1 3.644385e-03 -3.037137e-02 -3.142557e-02 -1.319176e-02
## netcen 4.247947e-06 8.553556e-06 -2.836475e-06 2.289602e-06
## updist -1.160792e-06 -1.516476e-06 -9.041883e-07 -5.879432e-07
## [,9] [,10] [,11] [,12]
## (Intercept) 1.193606e-02 -6.431572e-01 1.029606e+00 9.925732e-02
## avlength 1.017386e-02 1.482368e-02 -5.945561e-03 -3.107184e-03
## avcondition 1.420036e-01 -7.065401e-02 1.459360e-01 2.553069e-01
## T_av 3.532679e-02 6.659019e-02 -1.879500e-02 4.397943e-03
## O2_sat_av -1.109085e-03 -1.780974e-03 9.211405e-05 1.062527e-04
## Con_av 2.249288e-04 4.161917e-05 -2.084932e-04 -1.073458e-04
## COD_av -2.925292e-03 -2.013086e-03 -1.816569e-03 -1.936453e-04
## NH4._av 1.700026e-03 -3.587351e-02 7.085431e-02 -3.570309e-04
## Nt_av -2.464491e-02 -2.042881e-02 -2.756268e-02 -2.613938e-03
## pool_riffle1 5.149964e-03 2.434934e-02 -7.384338e-03 -1.940375e-02
## meander1 -3.592391e-02 -3.093900e-02 -1.011602e-01 -2.896967e-02
## netcen -4.890078e-06 2.155102e-08 2.116885e-07 3.898399e-06
## updist -2.556568e-06 -9.169728e-07 -4.186830e-07 9.989285e-07
## [,13] [,14] [,15] [,16]
## (Intercept) -6.368613e-01 7.803088e-01 -7.166398e-01 6.265197e-01
## avlength 1.681428e-02 -1.615996e-02 1.599244e-02 -4.870955e-04
## avcondition -9.852280e-02 1.116504e+00 -8.864468e-02 -2.266528e-01
## T_av 6.899743e-02 -5.281902e-02 7.019720e-02 -2.675075e-02
## O2_sat_av -1.154303e-03 8.408912e-04 -1.528332e-03 2.683281e-03
## Con_av 2.133819e-04 -1.628458e-04 1.210939e-04 9.193730e-05
## COD_av -2.256399e-03 -3.417431e-04 -1.905709e-03 1.685274e-03
## NH4._av -2.351319e-02 2.216998e-02 -3.305408e-02 -2.101423e-03
## Nt_av -2.236658e-02 2.414900e-02 -2.133049e-02 -4.373715e-03
## pool_riffle1 1.777876e-02 -1.073611e-02 2.225968e-02 -1.085855e-02
## meander1 -3.052164e-02 2.795040e-02 -3.051222e-02 -2.956997e-02
## netcen -3.753005e-06 2.977032e-06 -1.286598e-06 3.095126e-06
## updist -1.122169e-06 -7.836708e-07 -8.604971e-07 2.315622e-07
## [,17] [,18] [,19] [,20]
## (Intercept) -6.121613e-01 -6.298597e-01 3.672728e-01 -4.193382e-01
## avlength 1.697894e-02 2.055352e-02 -7.171231e-04 1.005516e-02
## avcondition -7.389408e-02 -6.777901e-02 -2.887636e-02 -5.762375e-02
## T_av 6.585840e-02 6.131461e-02 4.014584e-03 4.493748e-02
## O2_sat_av -8.101716e-04 5.594262e-05 2.683683e-03 -8.807157e-04
## Con_av 2.549962e-04 3.822115e-04 -2.856617e-04 4.572917e-05
## COD_av -2.152015e-03 -2.748748e-03 -1.553914e-03 -1.849240e-03
## NH4._av -1.868318e-02 4.450495e-03 7.811332e-02 -4.979537e-03
## Nt_av -2.305085e-02 -2.703083e-02 -1.925957e-02 -1.841033e-02
## pool_riffle1 1.402088e-02 5.461863e-03 4.427048e-02 1.431926e-02
## meander1 -2.516039e-02 -7.084940e-03 -9.584558e-02 -5.261891e-02
## netcen -4.556663e-06 -6.879221e-06 1.369068e-06 8.289165e-07
## updist -1.287669e-06 -1.451850e-06 1.280760e-06 6.116341e-09
## [,21] [,22] [,23] [,24]
## (Intercept) -6.532464e-01 4.100449e-02 1.704589e-01 6.462801e-01
## avlength 2.319031e-02 -7.043442e-03 3.719695e-03 -1.151131e-02
## avcondition -1.226451e-01 4.981712e-01 -2.975572e-01 2.656163e-01
## T_av 6.921670e-02 9.203739e-03 3.604248e-02 3.693320e-03
## O2_sat_av -5.363793e-04 -3.377174e-03 -2.104368e-03 -3.267513e-03
## Con_av 4.666038e-04 -4.483003e-04 -3.957322e-04 -7.538767e-04
## COD_av -3.665123e-03 1.139311e-04 -1.331837e-03 -1.247580e-03
## NH4._av 1.098478e-02 -5.822747e-02 7.918302e-04 -5.527844e-03
## Nt_av -2.846609e-02 2.913377e-02 -1.519403e-02 1.263706e-02
## pool_riffle1 5.976844e-03 -2.818818e-03 3.112259e-02 5.198239e-03
## meander1 -4.555456e-03 1.470428e-02 -8.750494e-02 -6.113122e-02
## netcen -7.704644e-06 1.084062e-05 6.382930e-06 1.250120e-05
## updist -1.047127e-06 5.517250e-07 -2.895584e-07 1.103984e-06
## [,25] [,26] [,27] [,28]
## (Intercept) 6.769474e-01 6.020722e-03 7.029508e-01 -6.495116e-01
## avlength 6.564463e-03 -3.847984e-03 -5.761959e-03 4.172387e-03
## avcondition -4.822334e-01 3.529973e-01 -1.612682e-01 5.898298e-01
## T_av 2.561781e-02 1.361892e-03 -4.982493e-03 4.612126e-02
## O2_sat_av 1.904383e-03 2.383360e-04 6.440399e-04 -1.955014e-03
## Con_av 2.945301e-04 -1.586756e-04 -5.006992e-04 -1.366137e-04
## COD_av 2.920903e-04 -8.000102e-05 -1.425778e-03 -2.044311e-03
## NH4._av 2.523120e-02 -4.970698e-03 3.710533e-02 -2.533041e-02
## Nt_av -3.061194e-02 8.474961e-04 -4.119371e-03 3.362813e-03
## pool_riffle1 1.748158e-02 -1.825549e-02 2.758115e-02 1.081138e-02
## meander1 -5.239307e-03 -2.501497e-02 -9.167294e-02 1.744373e-02
## netcen -8.945792e-06 5.603190e-06 8.449204e-06 1.762466e-06
## updist -2.988354e-07 8.492801e-07 1.866830e-06 1.009091e-06
## [,29] [,30] [,31] [,32]
## (Intercept) 1.664279e+00 6.279966e-01 -1.746030e-01 4.404427e-01
## avlength -1.236214e-02 -7.011657e-03 7.012405e-03 -1.778983e-03
## avcondition 2.858156e-01 4.234981e-01 -2.258974e-01 1.622057e-01
## T_av -5.986930e-02 -2.173927e-03 5.234007e-02 9.365206e-03
## O2_sat_av -3.463527e-04 -1.347510e-03 -3.029788e-03 -2.410160e-03
## Con_av -4.356963e-05 -3.400522e-04 -2.095022e-04 -3.845398e-04
## COD_av 1.130715e-03 -3.344424e-03 6.944179e-04 -2.171721e-03
## NH4._av 2.536027e-02 6.093858e-02 -7.590807e-02 5.297513e-03
## Nt_av -5.922196e-03 -1.726172e-02 5.007697e-04 -9.117520e-03
## pool_riffle1 -5.179915e-02 4.786601e-03 3.134113e-02 2.786768e-02
## meander1 -4.388545e-02 -8.632094e-02 -2.294827e-02 -6.533310e-02
## netcen 1.380107e-06 2.562367e-06 5.466809e-06 6.786009e-06
## updist -1.505678e-06 3.457645e-07 -1.297399e-06 -1.391436e-06
## [,33] [,34] [,35] [,36]
## (Intercept) 3.101886e-01 -2.542264e-01 -2.501988e-01 8.628936e-01
## avlength -3.162267e-03 7.673672e-03 -2.941552e-03 -9.795749e-03
## avcondition 4.018815e-02 -7.033370e-02 5.630407e-01 5.245065e-01
## T_av -2.895238e-03 5.432776e-02 2.745586e-02 -2.040254e-02
## O2_sat_av 2.413948e-03 5.390887e-04 -3.582597e-03 -2.035098e-03
## Con_av -3.367390e-04 -2.833619e-04 -3.887135e-04 -2.231771e-04
## COD_av -6.546298e-04 -3.187515e-03 4.168535e-04 -1.534188e-03
## NH4._av 4.520826e-02 3.397592e-02 -8.031235e-02 2.546175e-02
## Nt_av -1.636260e-02 -2.714229e-02 2.683761e-02 1.781403e-02
## pool_riffle1 6.586967e-04 3.365024e-02 4.205757e-03 6.631058e-03
## meander1 -8.882833e-02 -8.347203e-02 3.115884e-02 2.286827e-02
## netcen 5.008203e-06 -1.089956e-07 8.862842e-06 2.456590e-06
## updist 1.614076e-06 1.190913e-06 6.126184e-08 6.118538e-07
## [,37]
## (Intercept) -1.060510e-01
## avlength 3.588634e-03
## avcondition 1.124134e-01
## T_av 4.012527e-02
## O2_sat_av -2.040541e-03
## Con_av -2.358599e-04
## COD_av -2.983090e-03
## NH4._av 1.721446e-02
## Nt_av -1.453435e-02
## pool_riffle1 1.079718e-02
## meander1 -6.948440e-02
## netcen 2.399692e-06
## updist 7.900733e-07
##
## $f.perms
## [,1] [,2] [,3] [,4] [,5]
## [1,] 2.722531523 5.21174869 0.727412423 1.801691080 1.413524954
## [2,] 0.829082633 0.44866500 1.390122596 1.809911318 1.177274230
## [3,] 0.243683653 1.34467885 0.407694203 0.498946229 0.441320261
## [4,] 0.202895266 0.86657436 0.210269656 1.498158685 0.019049899
## [5,] 0.889758373 1.28038123 0.718313557 1.256777635 1.485339072
## [6,] 0.843675436 0.26075053 0.714965659 0.636728778 0.444388809
## [7,] 1.347152180 1.04154613 0.345972527 0.911616683 0.687946657
## [8,] 0.774031776 0.41380392 1.077717278 1.149371342 0.747734470
## [9,] 0.768404665 1.84841264 0.733277679 1.156950176 0.581540715
## [10,] -0.005148485 1.88780388 1.376966754 1.792548645 0.237932084
## [11,] 0.540555425 0.34626680 0.651145346 0.343817464 1.367790785
## [12,] 0.291213554 0.46940340 0.466770952 0.708663847 0.116579511
## [13,] 1.470117844 1.52938217 1.371213015 0.628970697 0.301800525
## [14,] 4.477660409 0.18770144 1.343814749 1.059070051 3.121673393
## [15,] 2.103304619 1.22412350 0.806694108 0.929245092 0.598755742
## [16,] 0.649426243 1.36991868 0.941887742 2.212728279 1.424006520
## [17,] 2.027402531 1.47289122 0.812892561 0.719637746 1.123235167
## [18,] 1.214519514 0.85014419 0.596021596 0.688675691 0.115034201
## [19,] 0.109750985 0.45266650 0.414196814 2.256468581 0.440316378
## [20,] 1.095447700 0.53423068 2.813417410 1.195597713 0.556442417
## [21,] 2.052125974 0.20151313 2.331216143 1.171040446 1.461442067
## [22,] 1.078779010 2.18972677 0.031270964 0.540947574 1.031891576
## [23,] 2.021210315 0.59937133 0.950023802 3.683133058 2.283025109
## [24,] 0.886531107 1.62972562 0.442661754 0.319314380 1.013481929
## [25,] 1.574837192 0.35384294 0.208450477 0.961476035 1.601736307
## [26,] 0.235575630 1.00156591 1.037274769 1.445365155 0.309517589
## [27,] 0.654441240 1.56662483 0.693195166 0.407999106 1.562114187
## [28,] 2.077517854 0.22755268 0.568833182 0.532306348 0.445977445
## [29,] 1.522203708 1.85098964 0.663131167 0.833495854 2.043276389
## [30,] 4.455577359 0.52106967 2.266031474 0.349586657 0.288509899
## [31,] 2.242655182 0.26386874 0.580914240 0.797950509 2.794623041
## [32,] 0.861448557 0.80211596 2.621799191 0.605966695 0.369615504
## [33,] 0.445059027 0.42240232 1.796463812 1.790787534 0.731860578
## [34,] 1.206812764 1.44521533 0.648879336 0.669895851 0.916318670
## [35,] 0.197149117 0.51033946 0.240674153 0.295339966 0.334197361
## [36,] 1.047318681 2.23481886 0.731540818 0.560897176 0.672113463
## [37,] 1.134509452 0.14733442 0.368893094 0.339704623 0.579117708
## [38,] 0.466871194 1.18439024 0.802191855 1.709256307 1.958827661
## [39,] 0.665114221 0.96975950 0.181278131 1.655517743 0.099398363
## [40,] 0.912251828 0.66618739 0.168072022 0.693359488 1.418249246
## [41,] 0.859573775 0.52591309 1.122074342 0.751998875 0.280118512
## [42,] 1.126940793 0.49107977 2.924304701 2.312873687 1.536573086
## [43,] 0.952750565 0.37191252 0.424486044 0.234014586 1.397677121
## [44,] 0.707865779 1.08067395 0.054073543 1.552541486 0.197563286
## [45,] 0.927074814 1.13121300 0.615262623 1.280496266 0.058105860
## [46,] 0.658674580 2.20816322 0.515208346 0.404637826 1.684557698
## [47,] 3.138514168 0.92446147 1.069470329 1.248142851 0.396869363
## [48,] 0.290252251 1.58494413 1.686009051 0.899239579 1.290249602
## [49,] 0.766620776 0.32395349 1.353404603 1.102428347 0.198950807
## [50,] 0.290365060 0.51002303 1.054681465 1.568629161 0.679049668
## [51,] 4.283850085 0.76926014 2.171437826 1.995539644 0.274555327
## [52,] 0.055658057 1.35186031 2.174618333 0.484059509 1.328534195
## [53,] 2.257689302 0.59946016 0.519800881 0.198423303 0.891784679
## [54,] 0.979773144 2.10749732 0.227509092 1.032150857 0.890674661
## [55,] 0.490845836 0.50841356 0.657854930 0.490632550 1.276867772
## [56,] 0.823847826 0.59067502 0.364837136 0.690482411 1.018004629
## [57,] 0.481895277 2.05523106 2.904096879 0.749410675 0.719894111
## [58,] 0.505558724 1.92317261 0.949861402 2.208680986 2.425643680
## [59,] 2.309394413 3.33821222 1.037265168 0.909712416 0.768442174
## [60,] 0.536797755 0.35563615 0.692785962 0.992873286 0.399913923
## [61,] 0.632847680 1.02487972 1.027291643 1.010795500 0.804145115
## [62,] 0.630848948 1.90152131 1.139116122 0.825668132 0.596401276
## [63,] 0.456767473 0.40470616 0.409564847 1.351530148 1.795408367
## [64,] 0.223792914 1.00451508 1.179725540 1.066890384 1.023009627
## [65,] 1.399415855 0.03304097 0.690731857 0.813563153 3.208031028
## [66,] 1.075878256 0.53496769 0.115463481 4.049132204 1.936826539
## [67,] 0.690656422 0.46414290 1.637369398 1.237195387 0.862205941
## [68,] 0.750287931 0.51919198 0.762365290 1.429638771 1.087952111
## [69,] 0.867717647 0.21154364 1.105340889 1.681052361 1.412125942
## [70,] 0.680764794 0.39796815 2.150882614 2.741180415 1.420567713
## [71,] 1.199553369 0.79883361 1.228299233 1.361895561 0.733803072
## [72,] 0.524296208 2.33892094 0.922729910 0.070782646 0.608990296
## [73,] 1.184047651 0.70422746 1.096038878 0.149876950 0.683514589
## [74,] 0.059122172 0.62104252 0.258628704 0.536362917 0.316668983
## [75,] 0.262365967 1.09968108 0.227610036 0.622418504 1.260722514
## [76,] 1.199357417 0.32842696 1.721521240 0.690972283 2.719816208
## [77,] 0.463232084 1.08524821 0.186066018 0.329223112 1.047812421
## [78,] 1.172297875 0.80259419 0.704259305 0.685377689 0.496038461
## [79,] 0.869812347 4.00132206 0.990986758 0.388609349 1.514256604
## [80,] 0.400233806 0.32134761 -0.007942901 0.389166229 0.543703692
## [81,] 1.384535061 0.06498657 0.467612307 1.892070410 1.077695511
## [82,] 1.121772304 1.34573236 1.208081696 0.775780430 1.384059051
## [83,] 1.438857703 2.27985422 0.889052982 -0.134638559 0.660661177
## [84,] 1.990823552 0.33575617 4.315281514 1.457388010 1.931879703
## [85,] 1.087512837 0.24516847 1.030826042 0.999348592 0.373933720
## [86,] 0.464191198 0.72515741 1.005569767 0.571566404 1.846769675
## [87,] 0.802755418 0.43436844 2.741623162 0.773730055 1.321577324
## [88,] 1.302741831 2.44623854 0.041217701 1.805745127 1.394509175
## [89,] 0.545374890 1.22114916 0.161076366 0.522413100 0.290407332
## [90,] 0.856464350 2.30123316 0.682273072 1.406964816 0.535804122
## [91,] 0.640202093 0.47327648 2.362075617 0.370480470 1.129869746
## [92,] 0.533878607 2.01281925 0.862810036 1.073172445 0.866201336
## [93,] 0.271242559 1.46672981 0.404034041 5.309219816 1.634929075
## [94,] 0.355354722 2.31680136 1.791464057 -0.029447287 0.198929785
## [95,] 0.382873935 1.58161123 1.355133395 1.296242564 0.235217350
## [96,] 1.212467978 0.37156934 0.089488940 0.211765523 0.582525225
## [97,] 0.520270373 1.32199364 0.646674463 0.878475773 0.179479636
## [98,] 1.390245445 0.94886127 0.139722835 0.416603234 0.711415818
## [99,] 2.365395438 1.30489477 2.054398713 3.121116543 0.677921002
## [100,] 1.224463737 0.68240776 0.333964664 1.623948964 1.265560534
## [101,] 0.506659713 0.74051082 0.075654952 0.770128878 0.578450038
## [102,] 1.818027123 0.33256109 0.380885136 0.831565589 1.388716191
## [103,] 0.399565657 0.57117092 0.468473922 0.169971368 1.334749632
## [104,] 1.423371325 0.29038230 0.837826388 0.236527647 1.043929921
## [105,] 1.249791903 1.45288834 0.696040760 0.300394813 0.893155613
## [106,] 1.755733715 0.66725312 1.047766014 0.421246360 2.167940066
## [107,] 0.257424378 1.15372313 0.732930169 1.799095607 0.526507769
## [108,] 0.790834481 1.61775851 1.391561092 0.302372127 0.537567014
## [109,] 0.624708410 0.65250517 1.577181069 0.751942630 1.273842182
## [110,] 1.009188717 0.67485068 1.731453366 2.009078649 3.742717598
## [111,] 1.072543628 0.51832163 1.151011754 0.828635666 0.246984491
## [112,] 1.382579461 0.37254256 1.874073981 1.339424759 0.410422922
## [113,] 1.673272155 0.29723605 1.126330129 2.881659751 0.730843313
## [114,] 0.537660767 0.28832686 0.705893315 1.695512859 0.545378378
## [115,] 1.355996695 0.90504929 1.253590062 0.582882607 0.438877916
## [116,] 2.764456139 0.41212654 0.531731499 2.053351595 1.312853409
## [117,] 1.346870103 0.45137594 1.238958159 1.794266348 1.137740099
## [118,] 0.609645693 0.55523590 0.600061553 0.833334944 1.071501857
## [119,] 1.076750714 2.19989759 1.190954309 1.497731079 1.557932548
## [120,] 1.895397854 1.31257474 1.528382741 2.012261775 2.314680869
## [121,] 0.524931004 0.48890285 0.457312307 0.441466464 1.060963975
## [122,] 0.085030063 2.05315708 1.332372212 0.067597578 0.428216372
## [123,] 0.595595988 0.25989843 0.524630542 0.677173308 0.239353612
## [124,] 2.252827441 0.79565551 1.604749669 2.984363550 0.803315006
## [125,] 0.774836944 1.82722428 1.657427948 1.337526408 0.998598202
## [126,] 0.383299862 0.82078272 1.084574440 0.379124142 0.663378329
## [127,] 1.573901320 1.67136948 1.724235086 0.667891710 1.609886364
## [128,] 1.001175247 0.39599034 2.307298573 2.998043682 0.832007414
## [129,] 1.534439171 0.26456335 0.282019522 0.631746780 0.206676744
## [130,] 0.550677329 0.72816148 0.691405035 0.513164088 0.673011045
## [131,] 0.612401270 1.82020458 0.801153341 0.841462512 0.971115737
## [132,] 0.237207712 0.69194082 0.860246364 1.037100368 0.594263494
## [133,] 1.492044428 -0.07341695 1.115939248 0.158605793 1.644760376
## [134,] 0.878082280 0.09281572 1.088148333 0.273339178 0.559311891
## [135,] 0.630184238 2.17225966 1.965615057 1.640822428 0.690307316
## [136,] 0.983912512 0.84958118 0.914265224 1.090424237 0.827186231
## [137,] 1.050807731 0.20394024 3.005620177 0.674096985 0.732600308
## [138,] 0.175229109 0.57579543 1.054494606 0.730532297 0.196641099
## [139,] 1.564019697 0.36679060 2.552741639 0.972773831 1.200469517
## [140,] 0.295337944 1.50352759 1.159491215 0.417808419 1.846316734
## [141,] 0.217130545 0.89720144 0.997408151 1.074275918 1.615027553
## [142,] 1.900594143 0.82257154 0.394975154 2.308244865 0.802425332
## [143,] 0.731884519 0.94641850 1.117299643 0.177102130 0.205553610
## [144,] 0.559427271 1.44877383 1.149053564 0.572468732 0.811502290
## [145,] 0.781933558 0.74196081 0.460272765 1.159136769 0.853063573
## [146,] 0.933537666 1.20433663 0.812580775 0.801877671 0.225234375
## [147,] 0.534903695 2.03436391 2.790018895 1.072941096 0.243052286
## [148,] 0.029437674 1.07191713 1.571322090 0.862258531 3.539013229
## [149,] 0.817742142 0.31165780 1.406345264 0.840718191 0.959755010
## [150,] 1.458928976 1.57810958 0.425977530 -0.028145408 1.644432819
## [151,] 1.273364120 1.28609585 0.783558185 0.950335884 0.538951421
## [152,] 1.034035797 1.30965026 0.230346260 0.547668537 0.473374075
## [153,] 1.203593357 2.31118095 0.676334830 1.872724003 0.259068226
## [154,] 1.410800692 0.14779316 1.234528321 0.445519531 0.351671748
## [155,] 0.117741443 0.23088584 0.748943770 1.118356326 0.792742907
## [156,] 0.486320922 0.18730596 1.356787512 0.717012792 0.909698765
## [157,] 1.114362331 0.77768930 1.200929179 0.560224855 2.760884604
## [158,] 0.332107642 3.44179757 0.881627333 1.364663992 0.135597832
## [159,] 1.364835430 1.92619599 2.234407010 1.988153296 0.784104050
## [160,] 0.748487993 0.40533975 0.150440368 0.168731866 0.335883799
## [161,] 0.874279590 2.40154274 1.157962221 1.241524503 1.059142778
## [162,] 0.444028658 0.17347528 0.044446632 0.652638057 0.199896585
## [163,] 0.964174536 0.30821308 0.639025578 1.264035861 0.224131070
## [164,] 0.147919955 1.17707105 1.164973237 1.280784579 1.251214482
## [165,] -0.046331359 0.21528942 0.712046061 1.498226128 1.260521847
## [166,] 1.061111527 0.60768249 1.750973066 0.543297640 0.445116782
## [167,] 1.268457547 1.66023318 1.140494073 1.648988823 0.453851030
## [168,] 0.780824226 1.83279148 0.138407076 0.926082234 0.936580922
## [169,] 1.719324214 0.35460450 0.144739372 0.385390512 2.244936628
## [170,] 0.430344933 0.22924746 1.109254475 0.760414267 0.459270507
## [171,] 1.181642194 1.62290554 3.364255963 2.446262171 0.324071535
## [172,] 1.989387967 1.80236204 1.694945792 1.143198158 1.007612862
## [173,] 0.488906468 0.71462122 0.563549373 1.241377933 0.744551967
## [174,] 1.346375573 0.55984016 1.126960102 0.662850958 1.081657956
## [175,] 0.467303351 2.03321429 0.492747130 0.716864391 0.403513384
## [176,] 0.976417924 0.93387906 0.531179296 0.966932520 1.263033234
## [177,] 1.334118586 0.35500738 0.740792114 0.060487699 1.648670293
## [178,] 0.371528406 2.18195487 1.457739951 0.561687722 0.822460038
## [179,] 0.701031458 0.28290224 0.205112418 1.133779997 0.799131670
## [180,] 0.811066094 0.34658605 0.309596200 0.921341678 0.166028247
## [181,] 1.442690503 2.86861167 2.512180008 0.834949040 1.456626931
## [182,] 2.279714010 0.42512361 0.727183195 2.026600106 1.039297253
## [183,] 0.623103958 2.77635303 0.768795656 0.044515953 0.790975229
## [184,] 1.265073147 1.14772882 0.923155955 1.322671746 0.674215711
## [185,] 1.175658078 2.03554497 0.519581927 0.125792952 1.240706641
## [186,] 0.046726772 2.22885336 1.352802687 0.145608413 2.163066415
## [187,] 0.682399790 2.57138286 0.874062029 0.228822843 0.679650981
## [188,] 0.211433320 0.53562246 0.402538091 1.625298155 0.415700307
## [189,] -0.022450817 1.12861353 0.813622364 0.375449752 1.296069023
## [190,] 0.640420953 0.08934773 1.301660929 1.097328668 1.565327168
## [191,] 0.061177923 0.59087350 2.932446887 2.545468322 0.550873707
## [192,] 1.119983076 1.26520422 0.739223617 2.444933563 0.971985391
## [193,] 0.881801652 0.03195050 1.220080726 0.766506760 0.506658981
## [194,] 3.909226267 0.81376542 1.625322899 1.124405324 1.039826037
## [195,] 1.271994588 0.71672509 0.604108843 2.726840047 1.304421812
## [196,] 0.334858740 1.35517831 0.327413084 1.533707807 1.027892443
## [197,] 0.984977472 0.74128726 1.247255898 0.657246204 2.988613398
## [198,] 2.075210693 0.43652155 0.912513894 1.056143589 0.600132778
## [199,] 1.028822117 0.82540489 1.460918287 0.388299445 0.714258592
## [200,] 0.314689200 0.20658338 0.563277869 1.208227837 1.040508805
## [201,] 0.498464699 1.15763709 0.838758825 0.641037404 0.898676345
## [202,] 0.260253818 0.19042525 2.424110378 0.954684781 0.708921361
## [203,] 1.925239615 0.86778866 0.057946378 1.809306930 1.337045845
## [204,] 1.304911475 0.73624219 0.087681202 0.659896384 1.550540837
## [205,] 0.823642352 0.92936941 1.498359789 2.244581328 1.936985510
## [206,] 1.515539371 0.98023588 0.893279608 1.146957692 2.812894250
## [207,] 0.358288402 0.17334123 0.267122672 0.296979377 0.753580961
## [208,] 0.527094866 1.26650316 0.201307026 0.424467740 2.107936050
## [209,] 0.732251010 0.72318074 0.478522602 0.135782621 1.220085552
## [210,] 0.567508968 0.35356489 1.494921576 1.815416046 1.204402646
## [211,] 0.274404545 0.99896828 0.602297927 1.216106604 1.275189714
## [212,] 0.262511133 0.88147971 0.946038755 1.724511827 0.646847079
## [213,] 0.559848026 0.53214302 0.609762976 2.689249552 0.513256110
## [214,] 0.705291995 0.77250007 1.010990552 1.099565896 0.777853084
## [215,] 1.137174704 1.69970713 1.083721088 0.452691918 3.094779720
## [216,] 0.411113237 1.42767576 1.301171559 2.310248246 0.499156292
## [217,] 3.148093286 2.10702639 0.317688726 1.085394754 1.500921045
## [218,] 1.321358184 0.79716268 0.198324902 0.890716862 0.816979909
## [219,] 0.278047901 1.34362377 0.289553493 0.562904734 0.449542518
## [220,] 1.387352370 3.36943571 2.083748312 1.351993005 0.656276243
## [221,] 1.001177857 2.48900021 0.317388925 0.098572855 2.080182169
## [222,] 1.742009687 1.08971913 1.363680463 0.835408800 4.673791860
## [223,] 0.533477998 0.33994885 0.899348507 2.334378337 1.113403827
## [224,] 1.526182000 1.73916229 1.469838845 1.046776372 0.346712797
## [225,] 1.007553906 3.76291250 1.414778210 0.547925128 1.256583646
## [226,] 0.505406557 1.61627176 0.988292898 0.459045748 0.714728361
## [227,] 1.299792199 0.57328975 1.058648251 0.660763204 0.628102196
## [228,] 1.558196236 1.19202232 0.735073942 1.170970573 0.154173648
## [229,] 0.873792161 1.26409177 2.045140682 1.686305329 0.477605507
## [230,] 1.048552980 1.04978722 1.321624678 1.959700302 0.693849587
## [231,] 0.817654216 0.12919110 0.547387694 0.150008981 2.071219634
## [232,] 0.640575433 0.52436002 0.983687932 0.031013674 0.287233990
## [233,] 3.941641976 0.61819882 1.520331475 1.029102555 0.110681273
## [234,] 0.045977046 0.33770047 0.683722912 0.065440515 1.372869680
## [235,] 0.772632146 0.39257725 1.540997644 0.853834402 -0.072265892
## [236,] 2.000187914 1.42325358 1.794060491 1.322786233 1.437034036
## [237,] 0.470469591 0.78132708 0.498720574 1.299991473 0.476750818
## [238,] 0.052042494 1.33536572 0.488692883 1.500455260 1.348400664
## [239,] 1.090692196 0.17031703 1.524884702 1.269020416 0.596650411
## [240,] 0.323395654 1.20167332 1.099442117 0.867361804 0.734061038
## [241,] 0.837666154 1.21568425 1.280521603 0.394258485 1.722036271
## [242,] 0.888152747 0.95402995 1.002539477 0.356513099 0.368929516
## [243,] 0.972868963 1.61184004 1.630149862 1.183263977 0.856087806
## [244,] 1.294630384 0.88345858 1.526314091 1.095780119 0.853040512
## [245,] 0.444585303 0.75673343 0.787453684 0.621630964 1.103089292
## [246,] 1.977974832 1.05489845 2.259468868 1.340245668 1.320195977
## [247,] 0.865075057 1.30136379 0.912684193 2.870407793 0.546871331
## [248,] 1.645744863 1.05287422 1.903182494 1.788211395 3.848899929
## [249,] 0.473724372 0.98205030 0.414573142 1.028927000 1.305807367
## [250,] 0.478433521 1.89064161 0.287266651 0.603153620 0.442784166
## [251,] 0.986362761 1.34623924 0.705680604 2.004831415 1.750448789
## [252,] 2.200625096 1.46242915 1.636601496 1.073763435 0.895551738
## [253,] 1.378111389 0.25819857 1.351275692 2.135618046 0.965694702
## [254,] 0.523087198 0.80925619 1.349475685 0.867901551 1.345357531
## [255,] 0.997694590 2.10667217 1.202696504 1.637135437 2.699275089
## [256,] 0.494128337 0.53499305 1.726028432 1.576797989 0.378286628
## [257,] 0.318074978 0.62849006 0.476344491 1.763445936 2.177845333
## [258,] 1.395104979 0.47997524 1.379638281 0.279055095 0.952090940
## [259,] 0.567430897 1.89335170 2.062646089 1.084218691 1.165502456
## [260,] 2.201246050 0.83125168 0.943293999 0.797076694 0.540850241
## [261,] 0.652745741 0.84908056 0.563559442 0.418387930 0.435327476
## [262,] 0.245717343 1.28389682 0.536969822 2.052264006 2.110222409
## [263,] 2.759605886 1.31645227 1.328295989 0.832701392 0.633119589
## [264,] 0.330690799 1.03009756 0.656553224 2.474476846 2.361870953
## [265,] 2.338814847 1.30589689 2.248172690 0.491239177 0.487996994
## [266,] 1.097475085 2.00694718 0.828301146 0.866544917 0.373660989
## [267,] 0.687460038 0.86488221 0.113049836 0.336828687 1.593757568
## [268,] 1.536012023 2.09493005 1.883399111 1.091583246 1.442085343
## [269,] 1.076033356 1.26660816 0.756469780 0.567919392 0.657656020
## [270,] 1.748580717 1.87438025 0.467388961 0.647475473 2.128674129
## [271,] 3.433131028 0.82388651 1.271337304 0.487546965 0.965021048
## [272,] 5.248397115 2.09856411 1.331356515 0.528301280 1.989694573
## [273,] 0.658565841 1.40590394 0.306864005 0.902200781 1.088943130
## [274,] 0.113819061 1.41375681 0.411532675 0.424369244 1.382901133
## [275,] 0.923620875 0.78823684 0.422858317 0.371317517 0.169117513
## [276,] 0.116678575 1.42599435 0.645870447 0.801218871 0.785680140
## [277,] 1.250820582 0.86335556 0.637501937 1.718051882 2.299758421
## [278,] 0.326281319 0.14543347 1.056373894 0.323240085 0.677092094
## [279,] 0.552015017 1.35717833 1.569050240 3.082088772 0.917273736
## [280,] 0.928030248 1.95454411 0.359889242 1.271496873 0.730335381
## [281,] 0.360655699 0.66922339 0.693256027 0.105196244 0.489399046
## [282,] 0.791883858 2.50701061 0.659713394 0.340241767 0.515219971
## [283,] -0.009145462 0.34521701 2.802571456 0.111630056 1.750078711
## [284,] 0.191485605 1.82783758 0.351277791 0.709068742 1.140656275
## [285,] 0.246259460 2.16579557 0.797820471 0.260111565 0.986269271
## [286,] 0.738252650 1.38767067 2.423663472 3.735044145 0.609236222
## [287,] 0.049875485 3.09607049 1.093869776 0.355285245 0.463724575
## [288,] 0.156832296 0.39782327 1.028411781 0.320453447 -0.047032540
## [289,] 0.838350569 0.16520488 0.857140261 1.387237731 0.387753545
## [290,] 0.470801267 1.38388508 0.744642922 1.327693028 0.357772648
## [291,] 3.423620854 2.03065325 1.148869803 0.914603293 1.684982508
## [292,] 1.052927207 1.26175950 0.813003634 3.461586470 0.459836723
## [293,] 1.831027558 0.57319583 1.370538839 0.546552739 0.762123078
## [294,] 0.471171396 0.45357027 1.275415076 1.212487588 0.848128380
## [295,] 0.659888960 0.32641627 1.028930548 0.932047623 3.446418491
## [296,] 1.936347552 2.51194113 1.985150165 0.378181193 1.776302699
## [297,] 1.208332019 2.57076815 0.964663996 -0.192658208 0.913710795
## [298,] 0.208021106 0.73741787 2.214930078 1.670323091 0.920205995
## [299,] 0.115095339 1.00993370 1.038912326 0.396243601 0.524585946
## [300,] 2.575517136 1.52823917 1.268881983 2.036406184 0.885753797
## [301,] 0.016233214 1.65717678 1.043779281 0.463439116 0.347308672
## [302,] 2.111320692 0.67618994 1.157397503 0.637871525 0.252712826
## [303,] 0.565940305 1.32993684 0.554928098 0.261965804 0.382840282
## [304,] 0.271885828 0.91890907 0.408513817 0.905025980 1.023628251
## [305,] 1.620569999 0.84451824 1.188972941 0.137426221 0.591663715
## [306,] 0.512455918 1.12837144 0.750524176 2.381090778 1.059279310
## [307,] 0.689533876 2.44711323 0.401080450 0.239897925 0.841661536
## [308,] 1.987169336 1.87624237 3.025348888 0.403400549 2.264300906
## [309,] 0.867543107 0.59244119 0.327387373 0.469262496 0.251641828
## [310,] 0.346353149 0.59211037 0.631902250 0.289937582 0.757115726
## [311,] 1.849033975 0.80321262 0.951225572 0.823875333 2.627317336
## [312,] 0.521291189 2.60530547 0.293630016 0.288742542 1.778804415
## [313,] 0.570137845 1.02245609 0.829382240 1.409525973 1.237137353
## [314,] 1.500166760 0.39975442 0.927534528 0.448232130 0.467371198
## [315,] 0.664896918 0.25159250 0.900986834 1.250449722 1.246316402
## [316,] 2.665898910 0.40368418 0.756177227 1.342702677 0.701702160
## [317,] 0.044501466 0.98136022 0.423481880 0.573762881 0.428432589
## [318,] 1.367453724 2.78908299 0.663228242 0.642847119 1.640650552
## [319,] 1.807210744 0.93391883 2.249575658 1.186727339 2.943753212
## [320,] 0.749109354 1.67682801 1.631495836 1.496052019 0.672484969
## [321,] 0.876569950 0.12106182 1.123473568 0.616972106 1.409359012
## [322,] 3.395335091 1.32479264 2.065338388 0.932901269 0.258283263
## [323,] 1.970559769 -0.07300254 4.454438390 0.864772828 0.738003906
## [324,] 2.116087042 1.86709059 0.747240823 3.573911185 0.942666196
## [325,] 0.316170711 0.07327517 0.766545341 0.315067279 0.619026957
## [326,] 0.255087585 1.06749756 0.190472150 1.160634312 0.353203423
## [327,] 0.973194492 1.43758998 1.926926758 0.771967656 1.521435536
## [328,] 0.991421778 1.50983380 0.452841026 1.107641641 0.305363627
## [329,] 2.002333910 0.87731448 0.820410163 1.814683300 1.389470002
## [330,] 0.623102509 0.01737947 0.344184970 0.088804943 1.399802074
## [331,] 0.877128557 0.94127100 3.361849036 0.297712938 0.755786248
## [332,] 2.167230183 1.09172711 0.631012195 0.504598859 0.546455581
## [333,] 1.704909142 0.63346795 0.194942457 2.882711557 0.682826811
## [334,] 0.878717513 0.46608191 1.720114947 1.652483479 1.273733878
## [335,] 0.746904918 0.98316129 0.399394603 0.552414488 0.657899229
## [336,] 0.190462066 1.41605873 0.915433797 0.085333548 1.045787464
## [337,] 0.310961019 0.59839589 0.743116306 0.391051503 0.994795885
## [338,] 0.590206979 0.62489462 1.892810285 0.657099502 0.823166382
## [339,] 1.360847004 0.75061999 1.669700719 0.921422736 0.699379654
## [340,] 0.551885800 0.69849305 0.597114915 0.894395119 0.523732755
## [341,] 1.064401246 -0.06469004 0.390687374 0.343747654 1.141141366
## [342,] 0.043180696 3.47537979 0.783162683 1.030938277 1.163253887
## [343,] 1.229655134 0.73363046 1.519260683 1.121440746 0.593861574
## [344,] 0.975548292 0.42871748 1.507881719 0.213360802 0.444770495
## [345,] 0.082523601 0.38519216 0.130604084 0.235694979 0.658771592
## [346,] 0.493963269 1.28369419 1.125073171 0.626085936 1.319957702
## [347,] 0.606891774 0.28346828 1.418532288 1.669290140 0.810532500
## [348,] 0.888872824 0.17601415 1.252686866 0.802604941 1.843965569
## [349,] 0.829553465 1.73683825 1.059582632 0.700774213 1.133622048
## [350,] 1.200377974 1.31447999 0.773034485 1.521339687 0.599808347
## [351,] 0.347616869 0.62825016 0.288329284 1.657029744 0.318295117
## [352,] 1.277399410 2.13201812 0.254270334 0.689644622 0.804993211
## [353,] 0.868577153 2.16120880 0.642528179 1.331870923 1.093541357
## [354,] 1.056980539 0.41135004 2.067675202 1.251498738 2.145847989
## [355,] 1.338472717 0.84694462 0.834926518 0.673280803 0.811014861
## [356,] 0.529707854 0.59078633 0.909084417 1.069736308 0.415689625
## [357,] 0.699108984 0.87367082 0.639546678 0.068077263 0.365850543
## [358,] 0.254883909 0.81579582 1.287882496 1.439876926 1.856379626
## [359,] 0.414953676 2.19603664 0.789489407 2.801997272 1.903720848
## [360,] 0.449907962 2.64520925 0.871958605 1.120293318 1.222478172
## [361,] 0.786221897 0.82357560 1.233269941 1.698936331 0.637280433
## [362,] 0.949719683 1.66712263 1.546197970 1.299940563 0.656046104
## [363,] 0.149956512 0.48443208 0.714579652 0.563572583 0.805086164
## [364,] 1.607217484 4.07259017 2.752152933 0.366448961 1.342095848
## [365,] 1.067409035 1.73397659 1.637527392 0.050072024 1.241752587
## [366,] 1.233492958 0.32821277 1.471062800 0.450477562 0.083021855
## [367,] 0.634972495 2.57182999 1.850568135 0.684503787 0.175864430
## [368,] 2.050893091 0.56998465 0.083271798 1.045555339 1.085465382
## [369,] 4.542305165 1.05864837 2.149898306 2.144721058 1.646342196
## [370,] 0.272770012 0.71386082 0.601876973 1.394152254 1.221896918
## [371,] 1.087477014 0.61878695 0.730013477 0.492540746 1.237665021
## [372,] 0.654782657 0.34203886 0.079267482 0.691780494 0.592966820
## [373,] 1.768202982 0.99159624 1.835990870 0.726910944 2.220863532
## [374,] 0.713764818 0.71400318 1.104904844 0.480901535 1.611536353
## [375,] 0.725858657 0.63093829 1.242756650 0.660057444 0.961153772
## [376,] 0.181122888 1.24864380 0.507323580 0.901061045 0.373594865
## [377,] 0.379966701 0.49275802 0.938710000 0.274666008 1.451259847
## [378,] 0.427728279 1.45689684 0.342166980 0.697478255 2.129192980
## [379,] 0.996399564 0.75268867 0.753356238 0.483032234 1.463716922
## [380,] 1.254507245 1.65113404 0.152785194 0.752850601 1.061916220
## [381,] 1.813531577 0.38501346 1.281638462 1.127741469 1.229728309
## [382,] 2.033948950 0.89541183 0.174655667 0.536197393 1.636379471
## [383,] 1.323668492 4.03686460 1.273411503 1.020118793 0.203787951
## [384,] 0.597146246 0.75803013 0.341710823 1.662988202 1.179241155
## [385,] 2.455659544 -0.05539079 1.130376057 0.860253367 0.749405607
## [386,] 3.375046550 1.89335179 1.431344996 1.036362482 1.505876861
## [387,] 1.538599151 1.66386032 1.545077732 1.180710910 0.358418049
## [388,] 0.676640737 0.90441295 0.622177238 1.686186654 1.104536989
## [389,] 2.224607317 0.43367377 0.101341301 0.617552399 3.052670910
## [390,] 1.387157767 0.34243227 0.567644191 0.203567613 0.700783003
## [391,] 0.610303387 1.15291224 2.167355249 1.130115286 0.945143336
## [392,] 0.436713610 1.79734234 1.021838967 0.938505413 1.349463444
## [393,] 0.209173395 0.21740686 0.541042433 1.830845573 0.257623826
## [394,] 0.784786986 0.75209658 0.515277165 1.504083779 0.670490749
## [395,] 2.358222398 1.13372397 0.729782231 1.163924397 0.804680867
## [396,] 1.120220509 0.97620048 1.268767815 0.930899055 1.446772213
## [397,] -0.084138939 0.24250767 0.869883042 0.253300312 0.640138211
## [398,] 0.596387352 0.48562466 1.497076092 2.075587996 2.362540974
## [399,] 0.516951327 1.29720574 0.291957409 0.555805600 0.399800728
## [400,] 0.718011310 1.67941442 0.655843986 0.691442109 1.340833458
## [401,] 1.497274844 0.64840523 1.516268569 2.285738909 0.655249498
## [402,] 1.207759803 0.19440768 0.124233131 0.844740614 1.365564238
## [403,] 1.819458273 1.06777324 0.534581200 0.214277654 0.639500570
## [404,] 0.219115782 1.22009972 0.198664427 1.302797819 0.540881017
## [405,] 0.124924376 0.78949537 0.396952516 1.545177776 1.871939616
## [406,] 1.404630221 0.20653349 0.122520408 -0.110604080 0.318246585
## [407,] 0.847557089 0.18185627 0.726440921 1.445525717 0.020216955
## [408,] 0.380638820 1.48371741 0.406770417 1.330004650 0.306896745
## [409,] 0.064504084 0.78335012 0.747759323 1.254952526 0.533270846
## [410,] 1.678222689 1.60030930 0.469397723 1.599240191 0.160987843
## [411,] 0.698072163 0.22069180 0.820173522 1.750420878 0.422093419
## [412,] 1.688288000 1.13076544 5.096545347 1.319512311 0.801763844
## [413,] 0.272114511 1.45580918 1.443464585 0.673996943 0.977749313
## [414,] 0.603026200 1.45239467 1.612083728 0.731312818 2.670169137
## [415,] 0.324484879 0.73276758 0.251704111 0.409959013 1.111905540
## [416,] 0.365337815 1.82619821 1.582358771 0.498096745 0.392740152
## [417,] 0.323541736 1.16813519 1.792599062 0.680742069 2.685606339
## [418,] 1.548019919 0.83170088 1.061366950 0.573998171 0.203588946
## [419,] 1.197274433 0.31828601 2.032391861 0.297419467 0.506814307
## [420,] 0.894325172 1.47324407 0.875492130 1.290568574 1.303302867
## [421,] 0.080928640 0.23070294 1.063348464 1.005399455 0.137551119
## [422,] 0.610545887 0.45131643 0.878464821 1.486996956 0.161119410
## [423,] 1.502630479 1.69298377 1.726372772 1.395956054 0.561851228
## [424,] 0.934085703 0.46271353 0.380865443 0.968971484 2.302288171
## [425,] 0.921551646 0.39796622 1.617511165 2.387623526 4.138964196
## [426,] 0.610682092 0.98442357 2.266667254 0.203524568 3.283872760
## [427,] 1.335298314 0.66927227 1.391379531 0.553739380 0.701538070
## [428,] 0.665995898 0.86875646 0.509864038 1.184870657 0.851475758
## [429,] 0.334003487 0.63640174 1.600320842 0.647951449 1.437663297
## [430,] 0.654376658 0.27427438 1.674570676 2.418790324 0.942352802
## [431,] 0.378032812 0.56402474 0.353668733 0.704942260 0.705531855
## [432,] 1.961250777 0.59817563 1.602627240 0.419145845 0.899644863
## [433,] 1.620381890 0.50614277 0.450895359 1.000447071 1.172048965
## [434,] 0.106872955 1.26140995 0.308570444 1.096710788 0.970284470
## [435,] 1.439925753 1.39718701 0.756250597 1.093919787 0.889405103
## [436,] 0.754093101 0.26393077 0.097620034 0.608935393 0.449407286
## [437,] 0.318907582 0.41493799 0.561459601 0.744533755 1.118845734
## [438,] 0.917853683 0.62533734 0.959731631 0.088307200 0.354115870
## [439,] 1.094308493 2.97201569 1.973376960 2.107693482 1.216627007
## [440,] 0.600762873 0.91743689 2.179722451 0.410974635 0.775516604
## [441,] 0.896785937 1.16413233 0.987756455 0.393682894 0.945305539
## [442,] 0.901238899 1.11051553 0.456335213 -0.061549959 1.623363348
## [443,] 0.504160222 0.50961238 0.338161738 1.239766760 1.122329922
## [444,] 0.663804269 0.46848856 0.713723207 1.094335241 0.555626327
## [445,] 0.116397161 0.77901042 1.103334898 0.552878196 1.086335102
## [446,] 0.875940234 0.56971741 0.335898993 0.464265691 1.608082309
## [447,] 0.263298725 0.95838162 0.334233418 0.374027988 0.628111662
## [448,] 1.404753472 2.51233936 1.910539391 0.182799062 0.815100244
## [449,] 0.561342629 1.12447162 2.216466242 2.364933418 1.613077599
## [450,] 0.211667450 0.39621112 0.137749273 0.945143662 0.955083021
## [451,] 0.903707170 1.24900910 2.259560086 1.622255518 1.303582700
## [452,] 1.175431479 1.25477652 0.060466160 3.356528000 1.333725593
## [453,] 2.680139258 1.31946106 1.627167791 0.844585960 1.711970342
## [454,] 0.053432361 0.90710911 1.415996619 1.673256060 0.929808978
## [455,] 1.060277871 0.08860473 0.147782192 0.949284014 0.859636848
## [456,] 1.253096165 1.71777160 0.532988934 0.067058731 0.873136356
## [457,] 0.519199305 0.54069421 0.715163717 0.997285911 1.603841171
## [458,] 0.827723244 0.62497632 2.062924783 1.367116496 0.969286422
## [459,] 0.184956424 1.06912676 0.463479103 0.372361120 1.470194875
## [460,] 1.037793486 0.57424833 0.885928429 0.380167317 1.170088252
## [461,] 1.293202417 0.23030694 0.363972008 0.827739416 2.403629171
## [462,] 1.539086789 1.30435597 1.338680363 0.706584593 0.027418513
## [463,] 0.464332795 0.18645461 1.383402186 1.353433202 1.981937495
## [464,] 1.973398642 1.22127126 0.857372711 0.986128947 1.527440944
## [465,] 0.633463650 1.94164009 1.229282636 0.892502874 0.372938082
## [466,] 1.784337028 0.75791963 1.213246946 0.093129163 0.475116663
## [467,] 0.322530549 0.29480633 0.793856181 0.491715860 0.724711753
## [468,] 0.392172455 0.31331454 2.410905311 0.690907339 0.602295861
## [469,] 1.167656620 1.07100717 0.991804921 1.560112739 1.454484422
## [470,] 0.721777180 1.26055392 0.628439390 0.373108885 1.332835887
## [471,] 0.441400148 0.19762617 0.502745508 1.505339768 0.580018411
## [472,] 1.815387460 0.57328888 1.130918360 0.579627117 0.583779020
## [473,] 1.155794594 1.12401754 0.516846749 2.358300959 1.009354359
## [474,] 0.336578796 2.03793733 1.490897133 0.907908046 2.130455640
## [475,] 0.769365381 0.85075301 0.282024115 0.389798991 1.087118068
## [476,] 1.712670661 0.84878959 2.323129733 0.626440483 0.924657771
## [477,] 0.781514362 0.49797868 1.115641798 0.191506907 1.073927612
## [478,] 1.371413776 0.45567714 0.316395865 2.367052337 0.783649801
## [479,] 2.565476164 1.81342844 1.929061941 0.961947645 1.091216955
## [480,] 0.900500105 1.10936814 2.418992339 1.466700854 0.578045200
## [481,] 0.655514640 0.99666015 0.397815464 2.018304855 0.555283565
## [482,] 0.474161175 1.33902784 0.210041196 1.053520421 1.500803708
## [483,] 2.513161091 0.63007069 0.193029183 1.066781303 0.411130414
## [484,] 0.645311115 1.50486768 0.085911444 3.347081249 0.831719365
## [485,] 0.248501493 0.30001016 1.094879086 0.665950488 1.752754128
## [486,] 0.548791534 0.21831755 0.876986309 1.582628669 1.387037188
## [487,] 0.426344522 1.25316456 0.376690076 0.014301507 1.034285084
## [488,] 0.999446517 0.59990205 2.197160009 0.306273187 1.557879145
## [489,] 1.009894548 1.84434009 0.360068517 0.650065021 0.041182570
## [490,] 0.982959874 1.06023119 0.570792076 0.220745644 0.122949512
## [491,] 0.952294527 0.66455830 0.593669241 1.308163662 1.425734622
## [492,] 0.389758896 2.13939055 0.174018796 2.305075237 0.153927541
## [493,] 2.711270068 0.89100666 1.100406155 2.512990877 1.783862810
## [494,] 2.832418031 1.62498549 1.078897764 0.605707437 0.784592567
## [495,] 0.809042839 3.60554273 1.384187473 1.561220778 0.439133160
## [496,] 0.298354115 1.53398889 1.655936138 1.654635244 1.795726979
## [497,] 0.801235197 0.73292735 0.775431892 1.595137371 1.423126694
## [498,] 0.410662607 0.76652022 2.510806986 1.623082201 1.318207010
## [499,] 1.294455400 0.43728694 0.952311771 0.197084706 0.763409515
## [500,] 0.747331810 2.05490179 2.144799515 1.622213178 0.191478043
## [501,] 0.445521677 2.10071931 1.574708750 0.566720013 1.482585487
## [502,] 0.986866830 0.54377322 1.148341250 1.104427719 0.188913011
## [503,] 0.492258879 0.86671598 0.078886704 0.761908789 1.959681017
## [504,] 1.136243362 0.52713013 0.860597358 0.493010234 0.923772786
## [505,] 0.108421376 1.81880066 1.342347657 0.944849380 0.867308896
## [506,] 0.549358909 2.67874543 0.390829459 0.775678499 1.064576126
## [507,] 2.176914604 2.54955561 0.184033910 0.406930420 0.490119604
## [508,] 0.716217164 0.84162633 1.336480872 0.901661260 1.381898565
## [509,] 0.545250817 0.56993084 0.943409490 1.231234591 0.766911451
## [510,] 1.454347718 0.90133311 0.450063248 2.424106062 1.086584307
## [511,] 0.771308014 0.38880537 0.260553423 0.945144521 0.609146129
## [512,] 1.016577066 0.35390760 0.251524924 -0.025595983 0.657815998
## [513,] 1.443566702 0.17038498 1.274068665 1.170161910 0.230069669
## [514,] 0.999471089 0.92914969 0.950075269 0.165374759 0.745518592
## [515,] 1.524666152 0.22953602 1.606681121 2.086556492 1.364343981
## [516,] 0.604612141 0.69921063 0.798838313 1.968832307 0.504070525
## [517,] 0.319401008 0.40954112 0.111431467 0.742603280 1.361015500
## [518,] 2.715269792 0.74501936 0.272615687 0.983965293 0.559961011
## [519,] -0.059494349 1.49655631 1.500875406 2.120711709 0.325564090
## [520,] 0.004489165 3.48726089 0.187036810 1.014936960 0.594211806
## [521,] 0.941941081 0.93601856 1.100256513 1.097870822 0.611269650
## [522,] 1.289782847 0.25975078 1.786975761 -0.017510557 1.171013641
## [523,] 1.855110418 1.34658278 2.328641397 0.381979499 0.398419719
## [524,] 0.200849090 4.61123076 1.198800438 3.206105666 0.281494301
## [525,] -0.123572844 0.41975663 0.356530939 0.613196975 0.387693825
## [526,] 0.421642465 0.68998345 0.836909280 0.219960992 0.535825777
## [527,] 2.859982661 0.62029987 1.707738111 1.308078427 0.059225515
## [528,] 0.175209507 1.10054447 0.130373240 0.932552869 0.365427475
## [529,] 1.568188618 0.53150656 1.398994419 0.238495648 0.255755229
## [530,] 2.947018832 1.28948665 3.025404285 2.700142386 1.421911118
## [531,] 1.353026635 1.25234440 0.527336605 1.214988582 0.811891979
## [532,] 1.664586044 0.85421211 1.022440133 1.107590997 1.851636207
## [533,] 0.637021783 1.10487939 1.513588732 1.438815758 1.383946730
## [534,] 1.088316749 1.18644044 2.309795908 0.345065578 0.474052124
## [535,] 0.161081151 1.25999873 1.474774047 0.749911898 1.719308373
## [536,] 0.701049233 0.90082301 0.396273608 1.277031359 0.944124394
## [537,] 1.226303185 1.22547668 0.981553780 0.703958203 0.420725304
## [538,] 0.766974311 0.40519396 0.437530979 1.096140069 0.549793637
## [539,] 0.622803185 1.21970569 0.833168018 1.637710458 0.815436820
## [540,] 1.850336456 1.67003218 0.465543598 1.844281987 0.665489915
## [541,] 0.953765675 3.32350134 1.177431865 0.223776864 0.969654185
## [542,] 0.896175472 0.40906861 2.109577374 0.756032841 0.513896353
## [543,] 1.311069047 0.94830078 0.613249570 1.008178807 1.072272384
## [544,] 0.623638237 1.28114770 1.024348836 0.585715627 0.798802582
## [545,] 0.430127161 0.63617653 0.742120505 0.702429175 0.004958808
## [546,] 0.957422506 0.15308859 0.902642935 1.446389253 1.098390145
## [547,] 0.119623077 0.85517914 0.712894268 0.492237078 0.635360416
## [548,] 0.597942590 0.30559836 1.135898587 3.741975691 1.269508847
## [549,] 0.588279266 0.53669480 0.674422199 0.677609809 1.316850145
## [550,] -0.065068735 1.25915896 1.054736516 1.330046879 1.171040664
## [551,] 1.142123301 2.61567493 0.578035976 1.698284357 0.921328571
## [552,] 1.238889371 2.11548199 0.316802948 1.520649693 0.033657844
## [553,] 0.764166324 0.53831944 0.376300995 0.886012718 0.695449869
## [554,] 0.324080381 0.90187373 2.153604863 0.558404583 2.107362210
## [555,] 1.544796126 0.95548561 0.094360520 1.722145642 0.506743460
## [556,] 1.363163932 0.48580554 1.188501594 2.147358873 1.120239214
## [557,] 0.562645552 2.08047750 0.400263552 1.192290242 1.536774036
## [558,] 0.854423664 0.75796855 0.600597793 0.364884718 1.331292974
## [559,] 1.859919335 1.21863707 0.571948799 0.948527811 0.038343720
## [560,] 1.353796134 0.84002820 0.331515353 0.630077401 0.624325241
## [561,] 0.384690306 1.39437383 0.660849632 0.583168727 0.478383841
## [562,] 0.803905491 0.39451882 1.676070572 1.470310040 0.243797969
## [563,] 1.030019332 1.21912175 1.147265276 0.976508506 0.107505277
## [564,] 1.301018363 1.22428422 1.385371822 1.937112098 0.605947810
## [565,] 2.013826959 0.41955533 0.183601832 2.070950439 2.112678639
## [566,] 1.762123344 0.45527425 0.554700268 0.926882723 0.136646657
## [567,] 0.392768371 1.46204107 4.050200863 0.420032903 0.739223408
## [568,] 0.342380969 0.52977636 0.877561852 0.270768279 2.072717622
## [569,] 0.422848712 0.91685470 1.642631155 0.485232666 0.410483203
## [570,] 1.505287281 0.27357200 0.783226807 0.387452469 0.216422013
## [571,] 0.461634701 0.43581871 0.652749521 1.193830615 1.326173373
## [572,] 6.827171899 0.79595195 1.936275310 1.817994277 1.763503334
## [573,] 0.686912328 1.43105745 0.533410981 2.239063849 1.740112655
## [574,] 0.899896382 0.56320575 0.601900409 0.293998565 1.285692736
## [575,] 0.294522194 1.57252541 1.384902628 1.525773838 0.506816509
## [576,] 3.719933861 1.10008499 1.017660283 1.216921866 2.329778015
## [577,] 0.679494766 0.72653475 2.167674510 0.718308183 0.036840132
## [578,] 0.164690931 0.42007972 0.643758489 0.595609062 0.541315320
## [579,] 2.426237057 2.04080601 0.124523161 0.899379796 0.839770405
## [580,] 0.512617819 0.69842747 0.593719674 1.771634910 0.397581855
## [581,] 1.904514495 0.62701734 0.118929807 3.743736527 0.564696028
## [582,] 0.461538815 1.70328025 0.702163819 0.831186145 1.875056320
## [583,] 2.443088618 0.83698606 0.892505493 0.362067767 0.231697152
## [584,] 0.807276329 0.86530982 1.865738767 1.546537212 0.862792200
## [585,] 0.617690804 0.18737275 0.618148586 1.930518015 1.650904242
## [586,] 0.336315356 1.12616619 1.178501972 0.407713655 1.099796771
## [587,] 0.102927171 2.37770633 2.375803723 3.211355851 1.529705031
## [588,] 0.589266831 0.13590623 0.507259239 0.506832572 1.142413634
## [589,] 0.906509999 0.52067060 0.630614509 1.575853757 0.852007730
## [590,] 0.639039863 0.05858754 1.368905856 1.075031468 1.318960900
## [591,] 0.681447493 1.99537549 1.167352359 1.009829387 1.054299208
## [592,] 0.471372093 1.49173665 0.469380832 2.217118845 0.252872742
## [593,] 1.157285946 0.94715424 1.380047117 2.282722502 0.120837964
## [594,] 0.659388650 2.07582386 2.286179070 1.549923470 1.546041613
## [595,] 0.164057529 0.21110317 0.905866405 0.346561465 0.686643237
## [596,] 1.065765628 1.63678494 1.292315281 0.634823366 0.460891682
## [597,] 0.779972226 2.67224094 1.929377493 2.354305268 0.499373188
## [598,] 1.431686720 0.68205844 0.883778343 1.620805334 1.066503099
## [599,] 0.782053179 1.47504526 0.688099816 0.125597338 0.723940800
## [600,] 1.468355477 1.30862679 0.429864455 0.524961699 1.617759385
## [601,] 0.455008754 0.48368405 1.100839760 0.595349978 0.266750086
## [602,] 0.044620899 1.29657099 1.445203644 0.658447298 0.300476530
## [603,] 0.976476872 1.74215576 1.905436505 0.510474698 0.332049120
## [604,] 1.936111486 1.26273818 0.439104386 1.100089850 0.145742283
## [605,] 2.079330325 0.51565917 0.299327794 0.577117585 2.160390807
## [606,] 0.055095379 2.12727861 0.373431149 0.627269616 2.053077656
## [607,] 4.002702115 1.65757278 1.107071294 1.769159487 0.401656613
## [608,] 0.381735967 -0.10966903 0.896854021 0.880508907 0.735166524
## [609,] 0.572364077 1.13356375 0.979481560 0.750466578 0.968729890
## [610,] 1.142720658 0.59439768 0.795420504 0.489030533 1.453028423
## [611,] 0.342085892 0.47227615 0.434453335 1.484826445 0.296425411
## [612,] 3.038939510 1.37887532 2.476047215 0.396524921 2.109250150
## [613,] 1.367014507 3.43259351 2.287668891 0.123131409 0.983199468
## [614,] -0.010046243 1.55589776 0.830514591 0.206933283 0.982366900
## [615,] 1.088048685 0.49163179 1.890484159 0.821277719 0.344260980
## [616,] 1.362707202 0.66890394 1.423907378 1.735487655 0.063839344
## [617,] 3.504685442 2.13226108 0.619580582 0.636329835 0.184107715
## [618,] 3.324133883 0.36978125 0.893830831 1.166166514 0.789392043
## [619,] 0.738176002 0.31730163 0.173273798 1.470698733 1.054160041
## [620,] 0.805984018 1.57202486 0.881331417 1.780008700 3.003641864
## [621,] 1.184521449 1.61270423 1.099180369 0.638247757 0.948181159
## [622,] 1.279389230 0.72437843 1.602241599 0.259908008 1.895989132
## [623,] 2.602215974 1.18256589 1.993279345 0.264198717 0.830055037
## [624,] 0.143250581 0.65100132 0.292755103 1.477790876 3.700405484
## [625,] 0.811127590 0.63052528 1.246634018 0.514143299 1.715524786
## [626,] 0.458981073 0.57378713 1.991280611 0.919944532 2.597064379
## [627,] 3.525336932 1.56049858 0.455338403 0.731536776 1.222323822
## [628,] 0.506925069 0.41480254 0.306487936 1.031885845 0.091442714
## [629,] 0.246820451 0.63322527 1.056836383 0.338677060 0.538416114
## [630,] 4.203447190 2.30019580 0.708937632 0.060839002 0.508392439
## [631,] 0.225022958 0.47367561 1.675290864 0.880958554 0.843816618
## [632,] 0.627769593 0.67052469 2.344306685 1.682373287 0.291492350
## [633,] 1.048267228 0.72314523 0.427835274 0.429770044 0.520241808
## [634,] 1.576550197 0.63254208 1.914152000 1.447123959 1.072482810
## [635,] 0.239490116 1.33954863 0.479610318 1.000560583 1.639438489
## [636,] 0.943386896 0.66607195 0.990842263 0.606723571 0.912251337
## [637,] 0.379535338 1.42600929 0.891156607 1.027884092 1.501336722
## [638,] 0.413985944 0.65097813 2.386103151 1.313965406 0.591501995
## [639,] 1.969489096 0.91393629 0.124183865 0.997064691 1.053930648
## [640,] 2.172827528 1.28152750 0.256079628 0.561320035 0.841656340
## [641,] 0.366205409 0.16467147 0.437940004 0.475125016 0.604432749
## [642,] 1.120109911 1.07092907 1.118915995 2.123920758 0.833590370
## [643,] 1.228413652 1.76039271 0.510754144 0.376211535 0.562357244
## [644,] 0.980773238 3.07858767 0.813461185 0.786048664 0.486989482
## [645,] 1.505376642 0.88838529 0.737621755 0.613103787 1.382159077
## [646,] 1.720801939 0.26734713 1.407425021 0.900392994 1.996624111
## [647,] 0.608182865 0.26265035 2.017578727 1.173227661 0.576404192
## [648,] 0.187502505 0.04016970 1.095179348 1.277752232 0.634715817
## [649,] 1.336255931 1.07751838 1.407088799 0.823570713 2.511559617
## [650,] 1.657504279 0.54216921 1.447771117 0.647427510 1.208249637
## [651,] 0.955159282 0.74416937 0.976616878 1.847320719 1.168357433
## [652,] 1.582488914 1.86906401 0.142754560 0.695803887 -0.049934105
## [653,] 0.795655662 1.07450847 1.242181855 0.922166106 0.224994885
## [654,] 0.196947231 0.79011107 1.091530012 2.027733426 1.253763796
## [655,] 0.225007279 1.01623522 1.503025110 0.530971528 0.568356076
## [656,] 0.559372377 1.02863520 1.256231809 0.710690795 1.220740579
## [657,] 1.101334519 0.87063656 0.519335931 0.306686523 0.642260710
## [658,] 0.199820305 1.79357296 0.391297008 1.584337457 0.825835273
## [659,] 0.077894422 1.30514915 1.426592099 0.996284795 0.240881995
## [660,] 1.046811728 0.73582830 1.592073017 0.298521770 0.235296511
## [661,] 1.189145798 -0.16370437 0.381086134 0.996804815 0.422022900
## [662,] 0.308365268 1.50842557 0.225863487 1.950119156 3.477643576
## [663,] 1.453598684 0.47445211 1.181752738 1.644962517 0.403661573
## [664,] 1.199208584 1.52276854 1.891008505 0.502685977 2.281575910
## [665,] 0.535600077 0.67612903 0.628751078 0.332043801 0.561567494
## [666,] 0.837905042 0.49402544 0.348826844 2.042576561 0.448634432
## [667,] -0.046670684 1.91584850 0.992937802 -0.004031198 1.094220977
## [668,] 0.796795641 -0.03790852 1.907317476 1.661557509 1.789844120
## [669,] 2.031391012 0.14101567 0.007737943 0.164314917 0.867127144
## [670,] 0.404118460 2.13425391 1.953136746 1.826275215 1.443430269
## [671,] 1.131288981 1.85381473 1.130187474 1.119212999 1.871103846
## [672,] 0.187527673 0.63937981 0.816174117 2.507955863 1.668960816
## [673,] 0.719606192 1.06935450 0.818074415 0.525769750 2.798491020
## [674,] 0.811749144 2.58940384 0.463840623 0.394190602 0.986710186
## [675,] 0.105654958 2.93699327 0.308453829 0.081564927 2.019514246
## [676,] 1.743032536 0.99657905 0.958466329 2.039405944 0.222465903
## [677,] 0.092385466 0.18695894 0.913273522 1.417577513 1.484288694
## [678,] 0.610731699 1.14459298 0.658550223 1.275342929 1.585842556
## [679,] 0.249578106 1.76543871 0.457554748 1.971467305 1.381532866
## [680,] 1.651712181 0.69874795 0.540041422 0.492825960 0.166962680
## [681,] 0.969191412 0.36851795 0.917312975 0.025262632 1.114960374
## [682,] 0.444731978 2.37903832 0.848790479 3.797283837 3.193942044
## [683,] 0.306220705 0.49917407 0.140027093 0.716201506 0.353858220
## [684,] 0.808796592 0.43177839 0.430541476 0.894533897 0.108612329
## [685,] 0.757383701 0.26177094 1.090427116 0.730722615 1.892844523
## [686,] 1.372741287 0.89744262 1.189666762 0.940784594 3.894605943
## [687,] 1.348182711 1.47340604 1.026287276 1.825654538 2.668051272
## [688,] 1.458263316 0.52465413 -0.079531898 1.720741364 2.438302188
## [689,] 1.285041029 0.45045856 1.168938862 2.341827960 0.449965372
## [690,] 1.952129573 0.76008491 0.916279960 0.200416883 1.452022316
## [691,] 0.409272362 1.18144984 1.018943713 1.671530780 3.510577681
## [692,] 1.391921063 1.60880030 0.554083449 3.023254089 0.819954724
## [693,] 0.798904829 1.76658351 1.532113201 1.097711884 0.978474177
## [694,] 0.920789161 0.19759517 0.188845140 0.333059806 0.686671677
## [695,] 1.062905711 1.75801883 0.849640883 0.833517786 0.437936461
## [696,] 0.336261518 1.37876633 0.037783902 0.818636728 0.799599272
## [697,] 1.038033328 1.91501498 0.995797562 0.639161505 2.066943732
## [698,] 1.529599218 0.21733167 1.191676230 0.981524850 1.895985085
## [699,] 0.420994654 0.31558632 2.091186732 0.543659385 1.459212910
## [700,] 0.753796853 0.65081717 0.479621568 0.367682890 0.914424427
## [701,] 0.595736722 2.36714767 1.901116208 1.078606026 1.267649283
## [702,] 1.403853124 0.46010310 1.159516648 0.942067176 2.867356962
## [703,] 0.122585254 0.36595510 0.626651053 1.372291411 0.590894999
## [704,] -0.170573922 2.73193678 0.340105192 0.580220266 0.746789999
## [705,] 1.277315913 0.59664477 0.436536352 0.440918290 0.325985269
## [706,] 1.093237302 1.48416385 0.925026718 1.067486233 0.269699106
## [707,] 0.414048789 1.47467831 0.794698649 0.043492154 0.310966217
## [708,] 2.108054086 1.31573265 0.386912573 1.267301763 0.400605837
## [709,] 0.675236333 2.42373797 0.681962596 0.150674960 0.917920014
## [710,] 0.208019129 1.12780617 0.401810002 0.845205619 1.205563560
## [711,] 4.412080754 0.99985938 1.429500580 2.729790084 0.621973450
## [712,] 2.492891020 1.10759094 3.168772440 1.274701943 1.518346744
## [713,] 0.123317112 0.20883789 0.340018605 1.708675680 0.263989308
## [714,] 0.822386429 0.66026793 2.427521450 0.799678793 0.382470719
## [715,] 1.308703296 2.16853273 1.157754509 0.359000273 0.329666077
## [716,] 0.975403285 0.71914784 1.018522146 0.457222423 0.965691387
## [717,] 0.137817801 0.61574545 1.771736203 0.884415084 0.729732672
## [718,] 1.251534312 3.06883032 1.030168997 0.936076662 1.311271536
## [719,] 0.703736178 0.29608205 1.637646425 0.025961756 0.364800329
## [720,] 0.440626113 0.71156613 0.573984334 0.078580984 1.645626318
## [721,] 2.393534170 1.02656899 0.814641736 1.273087843 0.358275027
## [722,] 1.107387558 2.68350237 0.698800040 1.196734203 0.712752147
## [723,] 1.174130008 0.95520302 0.603848692 1.634023002 0.831781147
## [724,] 0.918580168 1.11182641 0.937943047 0.278123947 0.624800781
## [725,] 0.099397164 0.75810419 1.139599132 1.134773302 0.262502699
## [726,] 0.977769936 2.50955454 0.549661544 0.247447096 0.211096760
## [727,] 1.882667775 0.36963750 1.994528135 1.165141244 1.073935784
## [728,] 0.339965665 2.07865709 0.462816827 0.558906039 0.120804075
## [729,] 1.739123115 1.03705984 3.509975046 1.302000268 0.889436032
## [730,] 0.602036150 0.86704300 1.930543895 0.267933920 0.534806042
## [731,] 0.607102855 1.09885437 0.574323176 0.166281312 1.240570384
## [732,] 0.351765535 0.80064255 2.593969276 1.950517240 0.422985992
## [733,] 3.532118167 1.41666295 0.869253479 1.562126954 2.046774021
## [734,] 0.483978004 0.67238720 0.411577639 0.403348591 1.024212456
## [735,] 0.588420433 2.09609303 0.472919168 2.212740289 0.896722703
## [736,] 0.481746644 2.83107470 0.241615396 0.702702702 3.692074024
## [737,] 0.792066813 0.87030600 0.663418621 0.225510648 0.332150615
## [738,] 0.612090894 0.85738358 0.953086711 0.940456983 0.539530661
## [739,] 1.250072005 1.20502491 0.933707617 0.513141753 0.916553365
## [740,] 0.864320089 0.71654880 0.921860970 0.409886847 0.569251927
## [741,] 2.640382931 0.91147867 0.780553105 0.271603839 1.322062201
## [742,] 0.746576453 0.56758147 0.679453381 0.304170769 1.139853445
## [743,] 0.618065877 0.74703550 1.035288891 0.179453960 0.605040682
## [744,] 0.271730980 1.96250194 1.560847098 1.627936220 0.580769784
## [745,] 0.101953989 1.04152189 1.429054698 0.776898761 0.737201155
## [746,] 0.907189549 1.02462706 0.733733852 0.784646852 1.421522817
## [747,] 1.672161636 0.63956334 -0.020746280 1.632956117 1.786667377
## [748,] 0.238226556 -0.13869107 0.783051650 1.016556708 0.445616035
## [749,] 1.695873543 2.55327080 1.838124675 2.523159481 1.224581538
## [750,] 1.002617660 0.10175099 1.273718125 0.181369127 0.915780115
## [751,] 1.012873442 1.33606424 0.854231135 0.659895234 0.395471559
## [752,] 1.245235025 -0.11190522 0.803154098 0.357873867 0.732647432
## [753,] 1.338993224 1.19338210 1.126724039 0.757348466 0.669834030
## [754,] 0.966987988 0.10943385 0.691315686 0.773267736 0.873135590
## [755,] 1.814301774 1.24816993 2.136249403 0.253503275 0.818974179
## [756,] 0.927982974 0.44472607 0.882713626 7.330711943 1.947146005
## [757,] 2.068536786 0.24416321 0.921788828 0.635792796 0.265582075
## [758,] 1.067087510 2.58151534 0.385283848 0.943816074 1.028287724
## [759,] 2.595017193 1.34832993 4.399182745 0.673196791 0.523901367
## [760,] 0.778297739 2.66130916 0.697676267 1.364234690 0.508938304
## [761,] 0.597621557 1.55776059 1.570661036 1.319556533 0.947798429
## [762,] 1.567685166 0.89132261 1.897463656 0.300541553 1.026156058
## [763,] 0.299659361 0.40156404 0.978854204 0.278412301 1.673995874
## [764,] 4.133339840 0.78969798 0.835348567 0.209613491 1.289331089
## [765,] 1.050478226 2.23790709 1.684848030 0.071900220 2.436481946
## [766,] 1.385813853 1.07977759 0.611483654 3.083501433 2.328377709
## [767,] 2.690461699 0.04843554 0.268483022 1.094902553 0.822744895
## [768,] 1.201484831 1.02920863 1.117733589 1.569706966 0.226330858
## [769,] 1.795180099 0.65740189 0.596783184 0.975937492 1.333599540
## [770,] 0.477459063 0.33651697 0.448210382 0.978668351 0.125822191
## [771,] 1.290066889 1.38176858 0.730018317 2.166495320 0.782786299
## [772,] 1.748604705 0.39696042 3.327266357 0.824665299 1.997830846
## [773,] 0.747001529 1.40363008 0.722514668 0.698701295 1.052312418
## [774,] 2.985346599 1.11378468 1.813800251 1.099611145 1.031435010
## [775,] 1.373516503 1.53373901 0.286668274 1.128769766 1.053414624
## [776,] 0.824528584 0.40332910 0.369662958 0.818036001 1.310636550
## [777,] 2.381144533 0.91792415 0.515503350 1.508025619 3.151075736
## [778,] 0.214226084 1.38696214 1.858893825 0.223158492 0.712578205
## [779,] 2.434180250 1.18265224 0.899519187 0.695157252 0.574366453
## [780,] 0.997519117 0.67839918 0.850782039 1.874167978 1.082807996
## [781,] 1.605616836 2.84109884 1.793079091 0.657073799 0.171060022
## [782,] 0.003087245 -0.03882673 1.712268475 0.542066299 0.664398615
## [783,] 0.217617027 0.87521814 0.938880088 1.062497010 3.108101407
## [784,] 0.827653273 0.67994696 2.516854509 0.369910253 0.122499043
## [785,] 1.354349747 0.32143726 0.462162379 0.670151190 1.333853675
## [786,] 1.740246453 1.27236417 0.428921900 0.329650814 0.035636688
## [787,] 0.353938572 2.08105792 2.274067927 1.664354623 1.384873427
## [788,] 1.098592356 1.68917665 0.818476929 -0.041914684 3.470524396
## [789,] 0.982805278 1.66855388 1.902854805 0.979434192 0.986932926
## [790,] 0.713588558 0.78372814 2.608600070 2.371746399 1.054863849
## [791,] 1.512940711 1.51584496 0.918657578 0.370407118 0.422693347
## [792,] 0.102760091 0.54840320 1.048376039 1.053029201 0.412864596
## [793,] 1.641285961 2.53845688 2.413415352 0.812513662 2.471918813
## [794,] 1.060168497 1.05978418 1.049224323 2.319273926 0.731915653
## [795,] 1.549172254 0.61389441 0.518023970 0.761995268 0.511151395
## [796,] 0.426484408 0.12960984 0.513285484 1.304177584 0.956595497
## [797,] 0.777161132 1.45020696 0.829718855 1.869583185 1.356129769
## [798,] 0.852710113 1.44954884 0.374429426 0.371619829 0.787249526
## [799,] 1.913886659 1.55691094 0.419029143 0.684050967 0.261784833
## [800,] 0.544033534 0.28863506 1.212961264 0.123216409 0.732752266
## [801,] 0.529891812 0.59221245 1.416085015 0.630979672 0.141334617
## [802,] 0.282537293 1.22673157 0.388990721 0.392346797 0.359950679
## [803,] 0.815593843 0.29604963 0.717660461 2.013351628 0.414440018
## [804,] 0.986738857 0.59178965 1.543405121 0.624068922 1.482273313
## [805,] 0.596836736 -0.05855630 0.786186152 1.136330417 0.645522402
## [806,] 0.740131001 1.58146229 4.428196992 1.261769412 0.067922198
## [807,] 0.574620800 1.16207387 0.438608348 1.873000173 1.389969630
## [808,] 1.084637221 1.11186597 1.035613830 0.581467482 1.266469513
## [809,] 0.392142496 1.11828805 0.973480289 3.498485153 1.275822404
## [810,] 0.841749830 0.81270801 1.519815507 0.905314387 2.490507361
## [811,] 1.373136857 1.29286179 0.422466447 0.686731559 0.817372227
## [812,] 0.410453774 0.63358619 2.062289668 0.673714218 1.389962468
## [813,] 0.668434387 0.86226455 1.473527908 1.088361653 1.088242907
## [814,] 0.570437686 0.42854133 1.198624830 0.749601692 1.384205583
## [815,] 0.778674564 0.67799712 0.714192440 0.286863647 1.526808605
## [816,] 1.090827663 0.86477781 1.199549975 1.914897264 0.667484877
## [817,] 2.343855629 0.27861072 0.836294306 0.104548111 -0.160305034
## [818,] 0.705585206 1.20912484 0.890636484 0.328077574 0.409242989
## [819,] 2.597640564 0.52921088 1.380661857 0.795355575 2.977581117
## [820,] 1.404349751 0.42526267 0.030512504 0.195728350 0.494008108
## [821,] 1.020505753 0.39097027 0.698282571 2.850514275 1.260015126
## [822,] 0.998105882 0.29840993 0.684227899 0.935776751 1.428236473
## [823,] 1.435311562 1.30804052 0.315019475 0.489566742 1.767145888
## [824,] 0.804863989 0.94540292 0.401715737 0.775450218 0.384896252
## [825,] 0.089784737 0.07985661 0.315155817 0.092275141 1.059485056
## [826,] 0.561262231 2.14504834 1.330027854 0.804745123 0.380432769
## [827,] 1.096025337 0.61096240 1.737625053 1.270091282 2.529142599
## [828,] 0.671327276 0.53652163 0.541775046 0.571918731 2.336879525
## [829,] 0.216071414 2.27501257 0.557270491 0.866397915 2.313220402
## [830,] 0.923622375 0.90887819 0.710491247 1.695747030 2.038288106
## [831,] 0.341464335 1.01277669 0.422886691 0.395712596 1.286769299
## [832,] 1.310938229 1.33327898 1.414750215 1.176503948 1.180495561
## [833,] 0.735468686 0.59200260 2.116506208 0.575876273 0.877535320
## [834,] 0.387948328 0.40573160 1.542318669 0.351209697 1.793844742
## [835,] 3.572905204 0.95205686 1.714822062 0.906491497 1.374775543
## [836,] 0.875045457 0.64071207 0.127623971 0.458444334 1.655211667
## [837,] 1.153670700 2.65338576 -0.032683605 1.541255947 0.609631953
## [838,] 1.718169034 0.57839409 1.413906964 1.525694514 1.204825451
## [839,] 0.537833419 0.66414201 1.257548997 0.533170762 1.484126604
## [840,] 1.094558177 0.13722713 1.512147662 0.556855434 0.667937329
## [841,] 0.670443466 0.97981199 1.394087605 0.633749283 0.305393757
## [842,] 0.094851778 1.52503558 1.565255501 0.883836847 0.662169217
## [843,] 0.432375226 0.94821160 0.383797735 0.676735137 0.243368014
## [844,] 0.746830796 0.73629961 0.370613057 0.522827578 0.810516778
## [845,] 1.113550287 1.59064361 -0.005159785 0.404701116 0.457293668
## [846,] 0.614652705 0.61452920 2.280226168 1.321284338 0.615674502
## [847,] 0.660814928 0.55778433 2.228417330 2.343184194 0.261162012
## [848,] 0.866458246 1.53913935 0.411740035 1.598768866 0.615788341
## [849,] 0.301360190 0.54310610 0.567102450 2.701159161 0.131862756
## [850,] 1.648215256 0.44210046 0.356895116 2.598212047 1.126145638
## [851,] 2.784001227 0.90555051 1.300561070 1.320442328 0.945996529
## [852,] 2.994568864 0.71498729 1.517256082 0.355316591 0.956224892
## [853,] 0.854304145 1.14370505 0.170368530 1.104205211 5.254475124
## [854,] 0.629678123 0.38149058 0.995182824 0.348446520 0.717515422
## [855,] 0.794874975 1.73895770 1.829025488 0.356979103 1.761897039
## [856,] 0.852826076 1.27146588 1.054693455 1.075148353 0.140110167
## [857,] 1.353713942 0.90805468 0.614923240 0.465934189 0.401392462
## [858,] 0.234338040 0.12148533 1.139730295 0.671489765 1.445378460
## [859,] 0.477926752 0.95711475 0.673333357 1.443568785 1.573034130
## [860,] 0.219203157 2.16570996 1.109541687 0.941469406 1.334038803
## [861,] 0.849564899 0.28920972 0.480961815 0.379915163 1.057720493
## [862,] 0.314140497 0.83422798 0.527398481 0.964199659 1.032782670
## [863,] 0.593915734 2.60721615 1.211372284 0.374729775 2.554342687
## [864,] 0.799083490 0.27666549 0.757823530 0.443575573 0.831703363
## [865,] 0.575920308 1.07677265 1.345396719 0.818700454 0.352859544
## [866,] 1.120764042 1.76182047 0.702306404 1.275615754 0.971580886
## [867,] 0.468117538 0.55608146 0.768765569 1.109150550 0.529343523
## [868,] 0.209425851 0.99283533 0.644384840 1.404687079 0.632532480
## [869,] 0.865815685 1.37305072 1.071883613 0.683869423 0.399630520
## [870,] 1.000921843 0.45176582 1.364381216 1.990061788 1.780806800
## [871,] 0.872669546 1.00980864 0.423130281 0.571982943 2.525495562
## [872,] 0.731241250 0.73148215 0.115214251 1.965184509 0.651041306
## [873,] 0.113688256 0.64036347 0.990347679 0.097827406 0.714494922
## [874,] 1.257973816 0.81528619 0.858581640 0.331043381 1.003313233
## [875,] 2.928893193 0.91315868 2.071547333 1.408693625 1.285672528
## [876,] 1.105932495 1.62373062 0.221928507 0.049335057 0.570854785
## [877,] 0.284845216 0.54055834 0.563336750 1.048592876 0.118916661
## [878,] 2.203796612 0.47655511 0.703748780 0.734023628 1.011228819
## [879,] 0.685201588 0.60667668 0.838119095 -0.092018075 1.285390989
## [880,] 0.968603533 0.92888087 1.161396929 1.298144578 0.322750254
## [881,] 1.596944961 2.67253669 1.159169793 0.537340683 1.188375215
## [882,] 1.009110728 0.78064704 0.870663228 0.775529208 1.313060992
## [883,] 0.571569597 0.51749909 0.364747095 0.704236264 0.465169364
## [884,] 0.708608439 0.87778300 0.827170816 0.536850746 1.128419449
## [885,] 1.482938929 1.52799019 1.217971016 1.918453329 0.873492830
## [886,] 0.597916923 2.01559831 0.062661365 2.610775909 1.580862990
## [887,] 0.099814011 0.73043586 2.101670762 0.825506182 0.780039098
## [888,] 1.537628080 0.91220956 1.140206759 0.266645994 2.140711964
## [889,] 0.362396028 1.06620647 2.039405981 0.099763721 1.022340395
## [890,] 1.336064427 0.38170112 1.373819881 2.911501603 2.429484740
## [891,] 0.951902833 1.40211995 0.711351207 0.667836870 1.191317219
## [892,] 1.291566826 0.39202120 1.893015964 0.828039001 0.851435919
## [893,] 0.569049532 1.00969677 0.461898591 0.292950599 0.904256801
## [894,] 0.431820589 0.88167305 0.388804865 0.343106685 1.026529926
## [895,] 1.072282871 0.73127833 1.702495867 0.560325258 0.914104432
## [896,] 2.284125404 0.34408636 0.554425826 0.301506636 2.845077302
## [897,] 1.642813289 0.51288556 0.436842501 0.857804597 1.256108816
## [898,] 1.587035676 1.03364459 1.662110807 1.043749599 0.758782156
## [899,] 1.255918463 2.22444847 0.388208014 0.429865240 0.619854987
## [900,] 0.975218767 0.41131925 0.621987589 3.751067372 0.125183521
## [901,] 1.066847271 0.58531522 1.595491405 0.283454320 0.753379408
## [902,] 0.207717280 1.90042140 0.601898734 0.215719126 0.722369865
## [903,] 0.500672049 1.55580752 1.204429235 0.569977857 1.464816698
## [904,] 1.088316167 0.72367426 0.742883671 1.135128212 0.591066538
## [905,] 1.496607180 0.85727866 1.108797477 0.955152407 -0.043711163
## [906,] 0.836579835 0.40471407 0.212855953 1.344389487 0.776601009
## [907,] 1.362615019 0.38628815 0.460843202 0.745102245 -0.151662972
## [908,] 0.532748118 0.32428521 0.360724895 2.008139463 0.825463695
## [909,] 1.565203376 0.04177778 1.079186271 0.987773796 1.101610920
## [910,] 1.017734247 0.99705417 0.976380685 0.630216398 0.597727318
## [911,] 1.406236122 0.61456201 0.768308707 1.099143615 0.261766120
## [912,] 0.629623700 0.58331125 1.719055451 0.119830505 1.088475654
## [913,] 3.122902428 0.69830358 2.189113919 0.163095264 0.215101617
## [914,] 0.623936911 0.35867109 1.179615314 0.591581143 0.676645527
## [915,] 0.657230513 0.36384916 0.608779549 1.666852626 2.698677829
## [916,] 0.332654655 0.99892829 0.333390331 0.729462185 1.800404103
## [917,] 0.659629319 1.68515489 2.747916592 0.607511039 0.486272068
## [918,] 1.302338395 0.55979605 0.734156436 0.661607067 1.158521354
## [919,] 0.407288648 0.49075281 2.240654524 0.355502620 0.905538371
## [920,] 0.730749958 0.17843986 1.540015794 1.058816958 0.189874465
## [921,] 0.451523010 0.66845880 0.239623596 1.385641816 1.132252053
## [922,] 0.932583701 1.86785505 1.530694787 0.833213975 1.587447960
## [923,] 0.559432963 0.87339123 1.823080908 0.505188224 2.675397761
## [924,] 0.629623719 1.28876053 0.301192008 0.467585612 0.885518284
## [925,] 1.765435120 2.00874466 0.250472518 0.591001805 1.743453645
## [926,] 0.614695450 1.38716688 0.861795909 1.056862829 1.328827913
## [927,] 0.519572851 0.08173334 0.876309339 0.761448105 0.627532088
## [928,] 0.960673264 0.42696567 2.806543005 1.532970497 0.755244643
## [929,] 0.972781688 1.64562999 1.023864898 0.276603903 0.586343583
## [930,] 0.451886637 1.01308150 0.501732962 0.042852181 -0.100596628
## [931,] 0.770476701 0.40779459 0.394561014 0.118675595 1.184084533
## [932,] 0.624846245 0.23103413 1.736927801 0.907218205 1.446688821
## [933,] 1.495293241 1.42305108 1.173540566 0.581500556 0.452491246
## [934,] 0.090253901 0.23887959 0.574216011 0.446908361 0.550221210
## [935,] 0.424861946 3.32563182 0.194789102 1.327006664 0.895597169
## [936,] 0.459652679 2.19815826 1.013724540 0.747380150 0.609249444
## [937,] 0.791408731 1.63982825 0.662208798 1.177682276 0.011343012
## [938,] 1.036029557 1.70766016 0.438662319 1.437490016 0.534721298
## [939,] 1.054880627 1.79884672 0.599861888 1.005308012 0.557253065
## [940,] 1.310199378 1.05215100 1.436075664 0.717633287 1.726839177
## [941,] 1.642602175 1.58794824 2.932367344 4.498444257 1.533527430
## [942,] 0.382561103 1.46205679 0.802717854 2.179375257 0.754579022
## [943,] 0.653525895 0.51574072 1.140456424 0.630210794 0.518770823
## [944,] 0.530553885 0.86199579 0.389212428 1.726429494 0.918853595
## [945,] 0.576237192 2.02414444 1.596299623 0.327111460 1.200289712
## [946,] 0.205695244 0.21718018 3.385019332 1.639031531 0.501529767
## [947,] 2.259921900 0.15492220 0.878015009 1.469627720 0.964893982
## [948,] 0.775791543 3.18453455 0.587925166 1.473716488 0.699103403
## [949,] 0.860134152 1.46535699 2.654447075 1.351866078 -0.028939365
## [950,] 0.511157285 0.62823945 1.305003945 0.390423012 0.716976052
## [951,] 2.046573155 1.33405753 2.031368161 0.431083147 0.409373939
## [952,] 0.327377526 2.04139676 0.738184626 1.080249306 1.481772407
## [953,] 0.638650617 1.68507589 0.939892312 0.515962221 0.699472673
## [954,] 1.266577524 1.62332160 2.684622294 0.794093824 0.422117441
## [955,] 0.491201246 0.29131395 1.009218891 1.516995908 2.431621811
## [956,] 2.090717845 0.25934582 0.711779749 0.588275277 1.313538141
## [957,] 1.513217367 0.33866938 0.569824892 1.557494622 1.021421277
## [958,] 1.603262619 0.45739163 0.272859303 0.413232434 1.167958975
## [959,] 1.128553210 1.12642713 1.143990165 0.711875858 0.978403374
## [960,] 1.038836354 1.10303464 0.342630508 0.485644396 0.657733312
## [961,] 0.964829991 0.37572165 2.155779852 2.839261953 1.110420114
## [962,] 1.325158455 0.56515923 1.088133985 1.132120209 0.838964890
## [963,] 1.756835117 1.32291676 1.760953526 0.764737939 0.736553065
## [964,] 0.492563294 0.28780880 1.213786697 0.933561170 0.755101891
## [965,] 0.141069927 0.53938032 1.048586806 1.010028419 0.418529984
## [966,] 0.283736023 0.68389335 1.196338234 1.571296631 0.578449310
## [967,] 0.268561513 2.61921880 2.348472422 1.861511264 0.746261278
## [968,] 0.311086037 0.28434188 1.626682464 1.133144101 0.403410967
## [969,] 3.544811122 0.72859988 1.153088606 1.648864116 1.175116768
## [970,] 0.550478837 1.71789520 4.943255072 0.352089799 1.581792496
## [971,] 0.919982738 0.33862766 2.839064869 0.299926338 1.324365146
## [972,] 0.257779861 0.41905212 1.533004195 0.867705857 0.618340077
## [973,] 0.994578150 0.58446264 0.602277601 1.987986134 1.392868594
## [974,] 0.672890242 0.35750938 0.410663635 1.317771188 0.447260103
## [975,] 0.346051427 0.53272318 0.590831603 1.340155139 1.814905227
## [976,] 0.951526793 0.75937346 0.661817448 0.392805273 1.372669996
## [977,] 0.525957338 2.34111753 1.007391425 2.270392958 0.193032985
## [978,] 0.316050283 1.25150053 1.237339741 1.744935741 1.955132893
## [979,] 2.539179490 0.41719193 1.231413683 0.583497515 0.999261672
## [980,] 0.724064686 0.33670288 1.963008202 0.868723059 0.809254893
## [981,] 3.764252412 1.82097823 0.581749888 2.902979409 0.505457033
## [982,] 1.388647703 0.81507355 2.810544246 0.821095867 0.432027724
## [983,] 1.784383638 0.84984567 0.789079621 0.167252117 0.870100106
## [984,] 1.082123725 0.81085931 0.641645049 1.268490939 1.361092594
## [985,] 0.375006319 1.28734995 1.938571442 1.664104490 0.825939968
## [986,] 0.565371655 1.30532857 0.339724855 1.999637474 0.080867188
## [987,] 0.397750832 0.90860848 0.613859304 1.001410059 0.905608775
## [988,] 0.422981973 1.01728566 0.745258737 1.109536966 0.624599651
## [989,] 0.656143795 0.87928841 2.306890045 0.068022294 1.431627043
## [990,] 0.722769461 0.33152798 0.251075991 0.901515373 0.913776133
## [991,] 0.989496853 1.73438286 0.907499160 1.077571419 0.071145035
## [992,] 1.651142177 0.40656608 0.688983985 1.572629979 3.066389481
## [993,] 0.173050450 2.28301840 0.937685215 1.870220113 1.908107823
## [994,] 0.460463231 1.25765649 1.687404177 0.476983446 0.332344611
## [995,] 0.885207685 0.83992695 0.501572569 0.791465302 3.868609403
## [996,] 1.674726524 1.45164073 1.328518422 0.587442561 1.695365896
## [997,] 0.531715602 2.15764923 1.293452693 0.232637419 1.416551700
## [998,] 1.276183113 0.76741073 1.024740052 0.944790230 2.400968932
## [999,] 2.349745959 1.45612920 0.459839940 1.165741111 2.547309737
## [,6] [,7] [,8] [,9] [,10]
## [1,] 1.2899374354 1.110680080 0.834448773 2.420180096 0.486254152
## [2,] 1.7519148312 0.837430880 0.668649990 1.842591155 0.804615795
## [3,] 0.5795004839 0.906067682 1.189284825 1.731635106 0.945028980
## [4,] 1.6969010557 0.042511599 0.514824063 0.771979696 0.373896923
## [5,] 2.0946580913 1.891896348 0.575030791 1.284653244 1.064441707
## [6,] 0.8171043767 0.094350576 1.577370783 1.871690809 0.458895046
## [7,] 0.3772002752 1.155763935 1.205151729 0.857267269 1.907720833
## [8,] 1.8586670525 0.058170576 0.951193361 0.488550856 2.635210676
## [9,] 0.7941454520 0.703020789 1.270445451 0.088489660 1.961500500
## [10,] 1.4067216273 0.812226461 0.318481367 1.060883152 2.658623123
## [11,] 0.8430285354 1.342575380 0.910045983 0.487646107 1.781064152
## [12,] 0.1134291108 0.536572397 0.756955149 1.291929251 0.122383413
## [13,] 0.4055054555 0.868967100 1.019356908 1.033962828 0.983386867
## [14,] 0.6251423434 1.422528523 0.531960091 0.170615403 1.403151589
## [15,] 0.8882987383 1.258673699 1.326308802 0.882005273 -0.099912507
## [16,] 0.7994052297 1.758529973 0.191689349 1.207225383 0.991251453
## [17,] 1.1257633108 0.116870239 0.571725730 1.156145507 0.225330450
## [18,] 1.2016589850 1.085061672 0.459641715 0.414608491 1.008758057
## [19,] 0.6070016183 0.911290097 0.437972622 0.465274977 0.361388749
## [20,] 0.8494472236 1.333096763 2.504333340 3.739759290 3.278909000
## [21,] 1.3381630009 -0.083409092 1.711746547 1.335965374 1.083170780
## [22,] 0.6702440701 1.112175047 2.594584032 0.217691753 0.623114021
## [23,] 1.6610204808 2.025616672 5.566927521 0.479806935 1.569939820
## [24,] 0.3549737492 0.841888189 0.643986730 0.316878187 0.965971668
## [25,] 0.6607506977 0.549977493 1.175477407 1.958327627 1.306972123
## [26,] 0.5184947474 0.494570098 1.206337372 0.701692337 1.390914947
## [27,] 0.6622726936 1.899077491 0.456554990 0.495214394 3.234434313
## [28,] 0.9903859173 0.631601899 0.444834611 1.772612813 1.220537329
## [29,] 1.4934681789 1.441724429 0.963189338 0.837712883 1.216782829
## [30,] 0.6677236788 1.230602671 0.161915057 0.691540989 0.631618131
## [31,] 1.0140643066 0.635922462 0.686249013 1.177259740 1.007673608
## [32,] 1.7326956621 1.710797413 0.535474601 0.740854421 0.590681473
## [33,] 1.3176973778 2.747081380 1.596831106 3.558460005 1.549918823
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## [74,] 1.4442372303 0.591697814 1.334463669 0.138101491 0.912937526
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## [77,] 0.4730844115 0.367774949 0.310021607 0.880226894 3.225935309
## [78,] 0.1075146630 0.486870913 1.101843189 1.903484755 0.654237898
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## [80,] 0.8817915448 0.284575311 0.625362382 1.852236719 1.525294916
## [81,] 2.3482238569 0.707659206 0.742687938 1.394005358 0.772496890
## [82,] 1.4971835562 3.878888772 0.512978583 2.368080854 0.557706855
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## [85,] 1.0128492706 1.129328804 0.902438146 0.328827840 2.555985278
## [86,] 1.3137537584 0.027410501 1.085673747 0.687516006 2.685516999
## [87,] 2.5749451639 0.305423852 0.486318212 3.099697641 0.354420089
## [88,] 1.4646303384 0.974236430 0.653424628 1.815188263 0.666291037
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## [97,] 0.8922674521 0.938926928 2.358187543 0.702458918 2.817961380
## [98,] 1.1160198170 2.512030614 0.394430830 2.251027970 0.575886532
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## [101,] 0.9846880661 0.783902853 1.074132599 2.443217737 1.160031148
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## [107,] 2.0644509639 1.405345561 1.881435265 0.504682325 0.575455952
## [108,] -0.0402843353 1.187797640 0.993609246 0.506902985 0.008039364
## [109,] 0.2160773269 1.337248016 0.962600041 0.134226954 1.149338396
## [110,] 0.9237874829 0.585970405 3.328282469 2.741670560 1.028780605
## [111,] 0.9187134762 0.839154448 2.278308433 0.759340222 1.486186563
## [112,] 0.0460711920 2.939880887 1.159513507 1.842595353 0.323034705
## [113,] 1.2421973572 2.354521404 -0.048067004 1.144824707 1.098419381
## [114,] 0.6054808918 0.891155707 1.288550494 0.490997121 1.092687367
## [115,] 0.7309865386 1.348722603 0.686605253 1.398845368 1.436348843
## [116,] 0.0891736519 0.453298400 1.018070212 1.929091674 0.701928415
## [117,] 1.1720367208 0.735625548 1.567095080 0.803183094 0.418338413
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## [119,] 0.9798760407 0.666080046 0.402803565 0.851064438 0.846750655
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## [121,] 0.4259525615 0.409159614 0.020751799 0.703166513 0.673994512
## [122,] 1.5594843470 0.294134180 0.328881450 1.138640700 0.910240755
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## [124,] 2.3169219595 0.173211579 0.945732338 0.293115204 0.026970174
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## [126,] 1.1439984353 0.568307589 0.297918827 0.850141674 1.058046205
## [127,] 3.1544694617 1.688842785 1.982077772 0.822473457 1.418840566
## [128,] 2.8499647419 1.692617898 1.193786989 2.564342104 1.054398433
## [129,] 0.2892241141 0.911337152 3.498051657 1.105338832 0.218408941
## [130,] 0.3863355635 0.860789352 0.326199208 0.607229641 0.636123387
## [131,] 1.0134769441 0.452157195 0.211683275 0.570695398 0.706107654
## [132,] 1.1218343466 0.293653407 0.912581881 1.353631869 0.773840693
## [133,] 0.2697791266 1.410022527 3.269433181 0.774674389 0.960590943
## [134,] 1.0499708205 0.365828708 1.140926705 0.446438788 1.803838135
## [135,] 0.5712051728 0.255106233 1.400145669 1.609612075 0.302871159
## [136,] 1.0095180872 0.536755927 1.496026123 0.995813312 0.327700948
## [137,] 0.9484442940 1.244380461 0.924715944 0.705512123 1.999749346
## [138,] 1.2068594687 3.551392399 0.770347163 0.095713894 1.054183712
## [139,] 1.3943255989 1.228949990 1.317070690 1.003778920 2.955971802
## [140,] 1.0645314884 1.109702851 -0.055974311 1.485094526 0.503751544
## [141,] 0.4861465412 0.296954956 0.635459133 0.749993850 0.779378301
## [142,] 1.7105741876 0.184686351 0.292069462 0.623161698 2.728883409
## [143,] 1.0005295119 0.593272196 0.583181401 0.610096026 0.758349649
## [144,] 2.6347938218 0.168228822 1.379161469 1.573578952 0.987100397
## [145,] 1.0156762705 1.723837996 1.040704057 1.506974073 1.964183521
## [146,] 0.8806834092 0.949393866 1.676312715 0.489937435 0.427296050
## [147,] 1.6843937416 1.196440129 0.940594713 0.943267894 0.497177693
## [148,] 1.1148805375 1.982168063 0.694264325 2.194617585 1.528863191
## [149,] 0.3336526632 0.755142154 1.361043035 0.523857991 1.256161158
## [150,] 0.4219745757 1.860756063 1.035552260 0.340789702 0.427385549
## [151,] 1.0489163369 0.259511131 1.725183592 1.339381780 0.849226120
## [152,] 0.7467158121 0.833128962 2.190820416 0.417315196 1.897370160
## [153,] 0.4431065560 0.981479862 1.271732460 0.512224519 1.030012622
## [154,] 1.1164773827 1.143179308 1.020106164 1.087844203 1.692027481
## [155,] 1.0956210294 0.865467253 0.173200363 1.563301797 0.178597156
## [156,] 2.1295359537 0.187288400 1.517062538 0.914587224 1.065212428
## [157,] 0.3995114253 2.338090072 2.168424931 0.467320869 0.867587465
## [158,] 0.3661300345 0.715492876 0.283114693 0.201914385 0.894600927
## [159,] 0.1759666506 1.061653901 1.054190192 2.571121490 0.521359479
## [160,] 0.4318371095 1.890778555 1.685878296 1.068843034 0.737537023
## [161,] 0.8809580061 0.187060784 0.597120414 0.163601107 0.076900498
## [162,] 2.6827963215 1.065365932 0.821487530 0.385649184 0.676178837
## [163,] 1.1377904245 0.874698214 1.043805775 0.998478416 0.603150749
## [164,] 2.4725830424 0.926781782 1.196403553 1.287619385 1.333191680
## [165,] 0.2239270837 0.181684542 0.264488106 1.369919766 0.666675160
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## [167,] 0.2701375701 1.156437129 0.624608056 0.694222288 0.293696006
## [168,] 1.0442070911 1.099047432 0.722311782 0.838072906 -0.017577989
## [169,] 0.8826838242 0.842344537 0.209327320 0.326149910 0.397758979
## [170,] 0.3576283045 2.684454124 0.784455365 0.413083660 -0.050696703
## [171,] 1.0740373741 -0.099241522 3.144742565 0.702974078 4.700581899
## [172,] 1.2347806926 0.356750583 0.681319908 4.227211216 0.631382061
## [173,] 1.0096925070 0.579436573 0.260367344 1.263548908 0.633702325
## [174,] 0.7897791962 0.642237696 0.260766307 0.818902396 1.763930987
## [175,] 1.5271255429 0.527994046 1.845538890 1.213408885 0.107581652
## [176,] 1.2619843410 0.500565584 0.987794559 1.816891207 0.479861867
## [177,] 0.8192428932 1.097533333 0.957462285 0.501287252 1.018551182
## [178,] 0.9537371359 1.084454449 0.523721236 0.225472551 0.390587988
## [179,] 0.4719305193 0.477295378 1.546879418 1.186233903 0.068885728
## [180,] 0.1702592166 1.301215318 0.970609800 0.656470027 0.886724736
## [181,] 0.4519125383 0.604794979 1.080420878 1.826585158 1.041844598
## [182,] 0.4790585162 0.934975069 1.583747982 1.006540668 1.351588511
## [183,] 1.7393240214 0.297116750 0.642579762 1.384223709 0.306009659
## [184,] 2.0548786588 1.071746978 1.151212522 1.068138037 0.874936285
## [185,] 0.5763402068 0.961134657 0.772290051 2.032763585 0.408152245
## [186,] 0.6006016710 0.810054128 0.395476485 1.814974081 0.893877719
## [187,] 1.3169285769 1.018719043 1.715887320 1.455641634 2.363589653
## [188,] 1.4911584644 1.226583390 0.985054439 0.350114727 0.538464478
## [189,] 0.6579512791 2.669059396 0.276329577 0.506450285 0.132077951
## [190,] 0.7008171392 1.137250559 1.047667741 0.369371746 0.544251181
## [191,] 2.0794681688 1.757968325 3.336728815 1.473758117 0.551124875
## [192,] 0.9243203661 1.765844314 0.531848891 1.179330246 0.153038161
## [193,] 1.2027634340 2.400841178 0.620511262 0.536208388 0.683772689
## [194,] 0.4073198174 1.300763235 0.764634944 0.784527350 0.680902373
## [195,] 0.3915615213 1.058721102 1.684698506 1.322974697 0.202005822
## [196,] 1.0474266197 0.146544550 0.645432364 0.388701936 0.432744336
## [197,] 0.3187506114 2.405277247 0.838364092 0.803425790 0.208949233
## [198,] 1.2085453758 0.595171018 0.353474777 1.148417159 1.071255885
## [199,] 2.0557975757 0.564971054 0.937231803 0.918597033 1.196027180
## [200,] 0.3950235087 0.401478007 0.055899908 0.980522263 2.534052892
## [201,] 0.5495610187 0.622504526 1.007956974 0.820663619 0.153780966
## [202,] 1.2356598735 0.342759159 0.590907937 0.916106620 0.289590296
## [203,] 0.6805160134 0.733701271 1.307888883 0.950509832 0.702319356
## [204,] 1.1353389129 0.452322618 0.457688969 0.008623916 2.834340103
## [205,] 0.3331448302 0.889985954 1.030182141 0.807422832 0.818063214
## [206,] 1.3657262390 0.834645146 0.621556321 0.828625687 2.692627372
## [207,] 0.2924095603 0.468801359 0.464258799 1.091273520 0.423851949
## [208,] 1.5305142713 0.209595905 2.676596801 1.128408765 0.426960776
## [209,] 0.9505338924 2.571256991 0.759509357 2.384583470 0.979484286
## [210,] 0.4838642988 1.450177415 -0.109445524 1.586975092 1.153056382
## [211,] 0.9066795872 0.351556058 0.802018616 0.609827093 1.440720332
## [212,] 1.6924730579 1.020768517 0.428860313 0.791432962 0.572995508
## [213,] 0.4293592817 3.348335163 0.784555837 0.170851783 0.754165864
## [214,] 1.7472613265 1.227926094 1.198246219 1.055487554 0.526458827
## [215,] 0.7984898839 0.915301426 1.072838452 1.100271131 1.342565957
## [216,] 1.6493518293 0.989522838 0.366537091 1.208991428 2.625511379
## [217,] 1.4059492252 0.703807598 0.193640435 0.518599162 0.583114870
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## [220,] 0.6458901705 1.798368728 1.514321737 2.071605089 1.888970148
## [221,] 0.9631156216 1.172481505 0.650455694 0.846998018 0.076413641
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## [223,] 0.5367207944 0.646147476 1.121546983 1.054236361 0.076325670
## [224,] 1.2499156797 0.696921419 2.221594570 1.885051771 -0.214737570
## [225,] 0.7196457358 0.048323232 0.079998030 0.907151245 0.262953824
## [226,] 0.7008733134 0.748559198 0.405873791 0.565832480 1.087078149
## [227,] 2.6750626668 1.371868621 1.822380257 1.574284751 0.016156304
## [228,] 1.5735490496 1.034074055 1.681191632 0.926789751 0.264561695
## [229,] 0.2980251965 0.089414471 1.140139914 0.283456423 0.818812245
## [230,] 0.5328642167 0.752403621 1.502009015 0.167171812 1.850813054
## [231,] 1.7095383404 0.359015615 2.633054601 1.814583863 2.856192769
## [232,] 0.1854780845 1.635254013 0.600675859 2.183134129 1.010394841
## [233,] 1.3347919188 1.208889951 0.693267093 0.537791485 1.511160392
## [234,] 0.7234304535 1.319749803 0.736040499 0.654829017 0.589687589
## [235,] 0.5091390441 0.465312413 0.445399898 1.150132697 2.003318006
## [236,] 1.1504904617 1.202003093 0.919633172 2.215164800 0.073696799
## [237,] 0.9121215493 0.713208193 1.687265682 1.020387990 1.189666510
## [238,] 0.1713138575 1.029485476 0.035691548 0.330008524 0.220130621
## [239,] 0.6160277271 0.583330155 2.119269086 0.177472437 0.706964391
## [240,] 0.5384357512 0.373416652 -0.044427747 0.250929956 1.198944910
## [241,] 0.3912882677 0.270642459 1.800475901 -0.001607672 1.500699890
## [242,] 1.1507135971 1.146141199 1.870844982 0.631076786 0.525359648
## [243,] 1.8009598183 1.169130179 -0.055348998 -0.040385773 1.698604679
## [244,] 0.3424336564 0.513964778 0.809060650 1.191930894 0.935894129
## [245,] 2.1337103394 0.271475728 0.389190489 0.945846599 0.435085042
## [246,] 1.6918871980 1.278665276 0.852396506 1.213249113 0.190938442
## [247,] 0.8156890598 0.990100902 0.616594205 1.682439462 0.362603760
## [248,] 1.3567586831 0.939584506 2.001115650 2.243364604 2.621910525
## [249,] 0.1254557506 0.367302499 1.709683440 1.583812443 0.281476511
## [250,] 3.3784521366 1.579124753 0.313057922 0.225306481 0.595585876
## [251,] 0.3156363733 1.997394876 2.136945054 2.806621477 0.954847260
## [252,] 0.7689702262 1.304733284 0.711846710 0.154646327 1.261278357
## [253,] 1.3316978159 3.161390426 1.828764658 4.294492498 1.109755853
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## [256,] 0.8780413922 0.550805666 0.395554159 0.174121733 1.241323635
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## [258,] 0.6227729411 0.679947522 0.111102222 0.757234937 0.270013800
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## [260,] 0.5142272926 0.516460724 1.699846034 1.288238525 0.560572613
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## [262,] 0.8198808230 0.353616160 0.591722496 0.719653827 0.063868331
## [263,] 0.5391924785 0.515118190 0.512424879 0.967189707 1.556121384
## [264,] 0.4918871478 0.271023966 1.648313181 0.847136736 2.733548667
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## [267,] 0.9377076674 2.624369609 0.859022142 0.897832643 0.318246776
## [268,] 1.4106937543 1.558593253 0.415398782 1.018109544 1.542548000
## [269,] 0.6364107215 1.687266335 0.859013699 0.866280431 1.658947642
## [270,] 0.7438449529 0.470044966 0.466176081 1.287039820 0.755989620
## [271,] 0.0255593534 1.694792807 0.279323362 0.753088123 0.776775852
## [272,] 0.2651516983 0.717165674 0.823092670 0.867776644 0.476478422
## [273,] 0.2687744592 1.226242426 1.289867334 0.889708657 0.687458161
## [274,] 1.2051597887 0.983871585 0.479346897 0.469389460 0.808289642
## [275,] 1.0935004264 1.232026418 0.747236323 1.375929800 0.787527929
## [276,] 0.7759568398 2.042467649 1.613862340 0.666881925 0.851309959
## [277,] 0.5364302031 1.185496024 0.134640305 0.611243759 0.657427700
## [278,] 0.3840993413 0.539746454 1.137658930 0.779149006 0.262564026
## [279,] 0.9552639775 2.717841663 1.306228363 1.350552791 2.685099267
## [280,] 1.2984762664 0.529802436 1.267485568 0.984321851 0.466749549
## [281,] 0.8288417692 1.480535041 0.650300110 0.269836777 0.588245297
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## [283,] 0.3093207651 0.643080405 0.622001924 1.925047627 2.204666379
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## [285,] 1.1498637497 0.094632361 0.484186549 1.849217073 0.434834919
## [286,] 1.2516279484 0.819039697 0.373704046 0.778987450 1.812994566
## [287,] 0.8286894731 0.952613972 0.816080322 1.420927786 0.702295522
## [288,] 0.3735351710 0.850324380 0.496897885 1.282253529 1.027780854
## [289,] 0.2645403564 0.753450368 1.379691628 3.210417146 1.050882639
## [290,] 1.2080041865 1.320776218 0.326174216 1.513802841 1.183922175
## [291,] 0.3377529109 1.333582758 1.005889982 0.374342448 0.683050593
## [292,] 1.1767499255 2.695998574 0.572023137 1.087799145 0.955258367
## [293,] 0.5961039781 0.661755738 0.508090460 4.076338703 0.248103718
## [294,] 0.1803671263 0.396763454 1.240106355 0.507693814 0.658485613
## [295,] 1.4809880452 0.367189671 0.281220372 0.962963241 2.855186751
## [296,] 0.6910787956 1.800534069 0.777172385 0.762576135 1.265347665
## [297,] 0.4096251085 1.587190853 1.039524706 2.730623139 1.346168868
## [298,] 1.8653158777 1.604864118 1.792684718 1.236084413 0.527060694
## [299,] 0.5753310482 0.231733316 0.295063271 1.744298898 2.216494184
## [300,] 1.8535094759 0.697235397 1.108531576 1.453174431 0.708405288
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## [302,] 0.7393853537 1.098662274 3.122387646 1.692097465 1.015057466
## [303,] 0.9749102724 0.863789613 1.117275433 0.920909569 1.633814125
## [304,] 0.1642250850 0.398728523 0.639463685 0.932288348 3.077852807
## [305,] 0.7152668360 0.797926215 0.368670113 1.720097043 0.732027372
## [306,] 1.1861586872 1.776011345 1.160979736 1.842411754 0.146617254
## [307,] 0.1319970485 3.183266910 0.853526421 1.383481218 1.739820255
## [308,] 0.0636293061 0.800773079 0.613298314 1.386620886 0.959072392
## [309,] 1.0861914336 0.977786156 1.365373250 1.135667495 1.443979445
## [310,] 2.3720142850 0.078313690 0.407992117 0.932189218 0.671955283
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## [312,] 1.6199880276 0.519679206 0.853424252 0.563518816 0.745307047
## [313,] 0.6095614043 1.083321285 0.216598157 0.892009040 1.253995841
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## [315,] 1.1078478533 1.772268312 0.448204150 0.362042527 1.754901894
## [316,] 0.7246197089 0.165514265 0.734781434 1.431159502 0.465097916
## [317,] 0.4755240178 0.255437188 0.928079902 0.226124125 0.212904619
## [318,] 0.8239340763 0.476172540 0.837991146 0.823748954 0.285070380
## [319,] 0.9220472957 0.830790382 1.496375491 0.739543456 0.414678691
## [320,] 0.6769872330 0.627651041 0.054128437 0.058235327 0.077536380
## [321,] 0.8277335220 1.326108814 0.735942920 0.634850012 0.051567133
## [322,] 0.1663029451 0.352712989 0.749095865 0.825003864 0.421780959
## [323,] 0.8223329172 0.805942850 0.568683537 2.955569010 1.420853882
## [324,] 1.0130645425 1.916119521 1.663892941 1.217130696 0.747908093
## [325,] 0.1899856372 1.408882499 1.089384490 0.200289816 1.301132506
## [326,] 1.5306979569 -0.007728883 0.890404170 0.397210270 0.490016103
## [327,] 2.2893767467 0.318454335 1.725825543 0.907123452 0.824644618
## [328,] 0.5272583534 0.528452466 0.781370727 1.436842610 1.465604480
## [329,] 1.3557771211 0.295668121 0.746308941 0.328887343 1.471356929
## [330,] -0.2387666766 1.693025587 0.894436284 0.469086034 0.651322483
## [331,] 0.2581662989 0.376892769 0.475893249 0.904753504 0.666346910
## [332,] 2.6891155457 1.468818714 1.085303887 1.160475724 1.361989752
## [333,] 2.0524668944 1.066827086 0.539706216 1.629994575 1.228670845
## [334,] 0.1283643571 0.373286378 1.030416276 1.915324313 0.991129212
## [335,] 0.4406455625 1.729308533 1.141002047 -0.006806497 0.956252833
## [336,] 1.0751146615 0.293851005 0.703219724 1.531679947 0.270405588
## [337,] 2.6366221749 1.208556894 0.909899010 2.739573836 0.312279444
## [338,] 0.0866368346 0.567908711 0.952618927 0.484394673 0.715903547
## [339,] 1.1751020146 1.593447987 1.065270699 0.630113897 1.176386152
## [340,] 1.1404386570 1.262448155 0.543860357 1.138234091 1.486707781
## [341,] 1.1521715615 1.110950550 0.415563772 1.635031615 0.829906125
## [342,] 0.6878985343 1.026873748 0.283587616 0.899897787 1.104057652
## [343,] 1.4401619313 0.266735291 1.039121762 0.274220686 1.306300444
## [344,] 0.5434021827 0.101005225 0.814193239 0.895308403 1.883687472
## [345,] 0.2244260752 0.620564398 0.595835154 2.134231789 0.457660095
## [346,] 0.2530861312 0.254799335 0.974663248 0.995456912 0.570184833
## [347,] 0.8710243741 0.550925224 1.450611696 1.554835070 1.105464110
## [348,] 1.4959448144 1.057383415 0.668921542 2.192244594 0.452128426
## [349,] 0.5896602843 0.751907282 1.748671031 0.564777301 0.031678678
## [350,] 0.0922683799 1.359486828 0.116223950 1.108716647 0.782406981
## [351,] 0.6649352910 0.691017037 1.741483616 1.567817009 0.876258959
## [352,] 0.6881420390 1.487931316 0.603301836 1.736963643 1.757917727
## [353,] 0.5180937622 1.807654676 1.079716002 0.259258734 1.372066684
## [354,] 2.3900088796 1.752678958 0.633524614 0.318648050 1.173195688
## [355,] 2.1886778595 0.339327324 0.468070335 2.333200009 0.350077326
## [356,] 0.4096380054 1.680284171 1.495011227 0.108151823 0.479490805
## [357,] 0.4982834017 0.768379904 0.224880608 0.912714798 0.795405646
## [358,] 1.6571629851 1.399828794 2.360622466 -0.111063033 1.589333428
## [359,] 0.1985143829 0.595968018 0.351307499 0.323975101 1.599321983
## [360,] 0.7455406671 0.497021549 0.466368831 0.604546001 -0.083715588
## [361,] 1.1145721218 1.757877168 0.645600194 2.656473050 0.942366931
## [362,] 0.9444505495 0.598511927 0.335092764 -0.033989986 -0.082718494
## [363,] 0.9807963361 1.089720457 1.917042294 0.330176865 1.823119015
## [364,] 0.1595949399 3.181590750 0.151620021 1.253043632 0.307455462
## [365,] 1.7256063686 1.682716707 1.668272851 0.923543040 0.218525031
## [366,] 0.7059573044 1.325625385 0.723136324 0.117258252 1.457522962
## [367,] 0.6648232186 0.732511362 1.626021044 1.040285273 1.237782884
## [368,] 1.5579678721 3.387758978 0.615651391 0.791895106 1.070191923
## [369,] 1.3354867117 0.363804843 1.479020491 0.140239985 3.703152667
## [370,] 1.1218124215 1.148191093 0.374365749 0.238523196 0.743511225
## [371,] 0.5228089549 0.358201983 2.975147173 2.725264236 0.766256013
## [372,] 0.8085240641 0.802511479 0.679138885 0.663154212 0.745832454
## [373,] 0.4891287756 1.496202726 0.966513203 0.596091071 1.040238802
## [374,] 0.3337984124 0.145142141 0.489486649 0.278834233 0.602389482
## [375,] 0.2695320876 0.345969632 2.270840834 0.802864233 0.400534520
## [376,] 2.6082127329 0.490637801 0.380085883 1.565475868 0.318259759
## [377,] 0.8793752016 0.621338160 0.322926673 0.329104693 0.818735585
## [378,] 1.2336873212 3.690980244 1.102409960 0.570787567 0.365442377
## [379,] 0.0695548143 0.665997128 0.295165136 1.424917208 0.814277684
## [380,] 1.4259631792 1.491642080 0.181398734 1.193109979 0.861592050
## [381,] 1.5558417596 2.008356328 0.593270501 0.333028493 1.971625335
## [382,] 0.3843485048 1.432052057 1.027549291 1.207954495 3.031596148
## [383,] 0.5482430631 2.302687687 1.000033780 0.526054893 0.560388569
## [384,] 0.4512693390 1.927284323 2.046065983 1.787911677 0.298373794
## [385,] 1.2303719995 0.533712653 2.911083489 0.749911865 0.518230628
## [386,] 2.1669301402 2.733034994 1.032659437 1.213352254 3.817383209
## [387,] 0.5540588284 0.543203034 0.530910357 0.460647000 0.646468710
## [388,] 0.4421433976 1.391315078 0.781701358 1.817304957 0.474056638
## [389,] 0.8168028862 0.084257139 0.192264030 3.688068807 0.178321022
## [390,] 1.4370648304 1.266943910 0.182239466 0.534196121 3.057080111
## [391,] 0.9722208971 0.489240832 1.284792105 1.128633438 0.664907283
## [392,] 3.4451150250 0.146854311 0.847511693 1.955905482 0.527481475
## [393,] 0.9242333309 0.774348302 0.958932277 1.262947137 0.779777247
## [394,] 0.8294472434 0.252133528 0.834187008 0.814311104 1.045823867
## [395,] 0.7247918913 0.768376610 1.876976868 1.439194664 1.301708349
## [396,] 0.3181842259 0.144796421 0.342571969 0.621705853 0.945660220
## [397,] 0.8585147133 1.446810556 0.522279490 1.202719565 0.211153250
## [398,] 1.1517126323 0.711732937 1.599485479 2.352903038 0.778520145
## [399,] 1.9452530324 1.028001070 2.520936024 0.534768458 2.981643102
## [400,] 2.0171098969 1.376879890 0.915747957 0.929358137 2.412046996
## [401,] 0.4527532521 0.914592161 1.739469743 0.606345221 0.464533279
## [402,] 0.9657401298 2.003348470 0.797094959 0.350172534 1.167627034
## [403,] 1.0050190026 1.461590079 0.365074919 1.592852838 2.392585369
## [404,] 1.0543850499 0.798138259 0.791038415 0.805908505 0.740251818
## [405,] 0.8302611002 1.419162358 0.608188177 0.988345716 1.325755677
## [406,] 1.1699984129 1.630459681 0.377609245 0.459863693 0.508308020
## [407,] 0.4501541230 0.476686670 0.259622561 0.553607123 0.613849799
## [408,] 0.8518458346 1.562959281 2.139338552 0.794507054 0.974452298
## [409,] 1.3890895158 0.188492099 0.872221554 0.432782270 0.943854892
## [410,] 0.5628404793 0.835063691 2.925131965 -0.017578575 1.456287922
## [411,] 0.6011246926 0.932199868 1.469589883 1.536560254 0.958903553
## [412,] 0.6807369077 2.039510437 0.910386223 3.118435485 1.178457684
## [413,] 0.5041085845 2.239151978 0.602781901 0.658017730 1.291075644
## [414,] 1.3692892848 1.231371991 2.677779542 0.247823074 0.791738932
## [415,] -0.0097507801 1.927488030 1.472581607 0.398929431 0.989611335
## [416,] 1.0093854819 -0.069074133 2.108460699 0.785140928 0.610682420
## [417,] 0.1640238635 1.311799323 0.351162724 1.268258186 0.721210479
## [418,] 0.3546356480 0.106036392 0.619447715 1.074298077 2.470068062
## [419,] 0.6542078767 2.826300031 0.107878697 1.429430994 3.449194169
## [420,] 0.2890772824 1.470949735 0.427487023 1.111993305 0.482954720
## [421,] 2.9171121538 1.531054323 0.422874468 0.263822177 0.098961854
## [422,] 0.4273022769 0.874480369 0.869986505 0.710490007 2.056742386
## [423,] 1.0173854990 0.542992173 1.207807368 0.772968018 0.688264749
## [424,] 1.1160861273 2.097534924 1.087775031 2.070417496 1.152175172
## [425,] 0.5424998923 1.018665837 0.694768067 1.557314208 1.611400775
## [426,] 1.8369722998 3.449177557 0.405655355 1.010160823 0.680805942
## [427,] 0.7252185353 2.529262772 0.881356210 0.881999051 0.607445066
## [428,] 0.8017932903 2.622480505 0.617939607 1.006755050 0.139311707
## [429,] 0.1776941253 0.092925709 1.454966582 1.310250896 0.274821609
## [430,] 1.1906042891 0.597640959 1.323089710 1.496602024 0.577887997
## [431,] 1.0538518861 0.085633647 1.684231590 0.605921257 1.044054232
## [432,] 2.0469181384 1.172876554 0.143661466 0.562395701 1.505372086
## [433,] 0.4413299806 2.450384017 0.388989884 0.446641687 0.575030209
## [434,] 0.9076449587 1.285053529 1.022208790 0.714759759 0.220553418
## [435,] 0.2391368336 0.610063748 1.515596898 1.639414380 0.776830505
## [436,] 0.6108005336 1.306998537 2.440432206 0.562138891 0.661356108
## [437,] 0.3181720246 1.135657428 0.038966450 1.121832136 0.299126290
## [438,] 0.7712866831 0.104890459 1.182671704 0.579206461 1.665172821
## [439,] 0.7680120547 0.939718785 2.343578395 1.854100935 0.023460337
## [440,] 0.2020921489 0.948917370 0.337176771 0.236459660 0.882549975
## [441,] 1.7628696577 1.625864877 1.911535583 1.136678600 0.701140900
## [442,] 0.3557584393 1.503092902 0.201968340 0.533129479 0.384970961
## [443,] 1.8643140169 0.997793771 0.579346875 2.305517522 0.556805640
## [444,] 0.7687338843 0.642429635 1.071731641 0.635322536 0.088473572
## [445,] 0.2208177623 0.713724040 1.145381863 1.027663460 1.222584968
## [446,] 0.6309421023 2.041027276 0.679668870 0.423665562 0.386396204
## [447,] 0.6470331094 0.611489885 0.543946960 0.591888760 0.463228209
## [448,] 0.0669937991 0.714597558 0.304666633 2.327521132 1.211196498
## [449,] 1.5721773953 0.963003989 0.532591984 1.452032097 0.458720972
## [450,] 0.5318155235 0.720661874 0.695773556 0.314454022 0.763981283
## [451,] 0.3510311799 0.720578259 2.050055102 0.518305716 0.590958450
## [452,] 0.4903969996 2.425910161 0.485997432 0.570000636 1.031151143
## [453,] 0.1652851229 1.057386992 0.590102107 0.632734197 0.713629800
## [454,] 1.0816449659 0.913337059 0.671071666 1.899047645 0.972771776
## [455,] 0.6624404786 1.310767531 1.025491904 0.668436272 0.611432235
## [456,] 0.1777471293 0.789076035 1.333306429 -0.011938698 1.418158111
## [457,] 1.1804156341 0.508814859 1.018020861 0.542480667 1.537544722
## [458,] 0.6726031395 0.927150143 0.958712957 1.365442229 0.122268351
## [459,] 1.2543608972 2.274922001 0.983366776 0.619852983 1.130163866
## [460,] 1.4134133714 0.751348063 0.710820142 2.786384512 1.143492314
## [461,] 0.1894686550 0.339692410 0.906048728 0.439355111 0.530481125
## [462,] 1.0877950590 1.078945464 0.308946531 0.265779852 1.211610914
## [463,] 0.9277441138 1.781770092 0.218310930 0.893591054 1.520912699
## [464,] 1.2237209442 1.363681592 0.543379726 0.390761416 0.357738537
## [465,] 2.1340004628 1.237833539 1.545705618 0.574994013 0.678917105
## [466,] 1.1205872792 1.318915957 0.284148190 2.511044275 0.947912361
## [467,] 1.0399491341 0.260345693 0.934124764 0.414658779 0.873394882
## [468,] 0.2990985221 0.753367707 0.205573039 0.976511266 1.774663163
## [469,] 1.7707860203 0.602066610 2.222396732 1.266355906 0.476742094
## [470,] 0.2953353502 1.328468188 0.354600134 1.164127644 0.571845483
## [471,] 0.9215194391 0.463456955 0.463591125 0.678766649 1.252587263
## [472,] 0.4207879561 0.047660347 1.514910126 0.952147966 1.974438779
## [473,] 0.9999319789 1.419336736 1.880786756 0.796802073 0.450128322
## [474,] 3.7662862790 1.100570133 0.599040505 2.333490113 0.138866183
## [475,] 0.2545872852 0.122580357 1.578938026 0.458216259 0.476220185
## [476,] 0.2499992898 0.725828219 0.965791488 2.283908739 2.141458408
## [477,] 0.1598628942 0.018021163 1.872742973 0.458841530 0.982954538
## [478,] 0.9608062112 2.589040980 1.169043139 1.652386258 4.412400885
## [479,] 1.6694109016 0.324307819 0.773229661 1.526999296 0.236528091
## [480,] 1.5269757619 0.898307192 1.787885392 0.072191492 1.078540812
## [481,] 2.1600077218 0.030906917 0.721153555 0.813326978 0.826777846
## [482,] 0.1058173495 1.002095814 2.139414937 0.596341548 0.258139795
## [483,] 1.3238648818 0.843478586 1.266190096 0.452592905 1.196349346
## [484,] 0.2754731209 0.590774852 0.309066526 0.454054216 1.858074333
## [485,] 0.7372845848 1.524512035 0.213688200 1.877782009 0.370711419
## [486,] 1.0173751817 1.664041072 1.110289394 1.019850241 0.251909631
## [487,] 1.5486838400 1.810912959 0.556604211 0.278372356 1.972678235
## [488,] 2.6911875982 1.050431777 0.634386066 0.504398943 0.971312885
## [489,] 0.9486810730 0.807479023 1.077328963 1.178773397 0.250926890
## [490,] 0.6868232267 0.973818263 0.425913227 0.661336387 0.472800226
## [491,] 0.9495022981 2.184478466 0.663625402 2.176062931 0.641160860
## [492,] 1.3937131022 1.377834076 1.577598191 2.456602777 1.644905744
## [493,] 1.6642926659 3.707365528 1.412196306 0.709060423 2.237456535
## [494,] 5.6131334794 0.546116895 0.987459488 0.098573302 0.850865165
## [495,] 0.9878120105 1.362289111 1.591486984 1.323956250 1.061963199
## [496,] 0.4364905086 1.257961174 1.457163131 0.811033740 1.375290120
## [497,] 0.7018212336 0.768188555 0.583640906 1.890218018 0.260382694
## [498,] 0.7431768217 1.422883074 0.543034270 1.225489954 0.774898093
## [499,] 1.2304841660 2.396126765 3.901594408 1.027245914 0.921178227
## [500,] 0.2054887391 0.493017503 0.628061009 0.817994729 0.977202632
## [501,] 0.6191795006 0.272979260 0.740385208 1.802262742 0.233953135
## [502,] 0.0935800120 0.135948077 0.781659361 2.064530957 1.808397954
## [503,] 0.6936211746 0.231378286 0.897192952 0.220043152 0.594565695
## [504,] 0.1860318088 1.198703491 0.400704396 0.140231610 0.264236448
## [505,] 0.3660855963 2.314918695 1.361092139 1.147373182 1.677671805
## [506,] 0.8994500494 1.180001640 1.900250969 1.272673378 0.520346863
## [507,] 2.6178199332 0.143131866 0.588044029 1.716581863 1.171188632
## [508,] 1.3216673839 2.464932620 2.567699249 1.427774712 1.412299479
## [509,] 0.4870544583 0.404930792 1.630491528 1.550503287 0.584917368
## [510,] 0.5123810923 1.218776553 0.445717571 1.826292473 1.617802653
## [511,] 0.4217636691 0.637434938 0.231764968 2.086750837 0.698095464
## [512,] 1.6111536592 0.697406584 0.967315876 0.596105121 1.162072397
## [513,] 0.4824415068 0.044170035 0.626685151 1.457062936 1.450074244
## [514,] 1.8775539804 0.319184781 0.604725337 1.570028923 1.182431007
## [515,] 0.3017865069 1.126418719 0.577933261 0.357533352 2.057157071
## [516,] 0.6437358086 1.485996344 1.380355852 1.173789071 1.138649834
## [517,] 0.4748666271 0.243393792 1.131752149 1.423034187 0.457529187
## [518,] 1.1468107043 1.329718494 1.646512032 0.241586461 0.824421790
## [519,] 0.1878411675 0.160778353 1.492217524 0.775309269 0.999627071
## [520,] 1.0071622667 0.750713922 0.040703436 0.485462699 1.942975723
## [521,] 0.5005606461 0.593670561 0.754531288 2.030891938 1.003319187
## [522,] 0.7444325073 2.722869307 2.798096969 0.920688095 0.575838963
## [523,] 1.6781844682 0.916855836 2.245208194 0.450073801 0.148892512
## [524,] 0.8672939243 1.107155293 0.559773239 1.390181785 1.920888773
## [525,] 0.3060499918 1.589167579 0.363173299 1.184536412 0.738430107
## [526,] 3.2879620262 0.112778467 3.136102594 1.122627926 0.545863797
## [527,] 0.8926891350 1.290465362 1.347433826 0.301613435 1.054645218
## [528,] 0.3746072314 1.078885898 1.123556140 1.159850773 1.929252393
## [529,] 1.7622089563 1.026378931 2.349671964 0.590507304 0.580582388
## [530,] 1.3886214225 1.961373012 2.554008177 3.437091617 1.083565094
## [531,] 0.3757420476 0.430700815 0.542565358 0.577933792 0.322298098
## [532,] 0.4021791996 0.449464316 1.380636049 1.374861191 0.686046336
## [533,] 2.2522640727 1.049026657 2.201829985 0.943218034 0.990023934
## [534,] 0.4915158665 1.031853665 1.330536282 0.877642974 0.124023108
## [535,] 0.2460453632 0.791678982 1.222214987 1.719349913 1.344562205
## [536,] 0.3237432283 1.598475276 0.705649782 1.524790274 0.999966346
## [537,] 0.6464689824 1.069142072 3.119695076 1.249468007 0.825789895
## [538,] 0.8653088355 0.551286803 0.415711083 0.812189097 0.999179047
## [539,] 0.2564912737 0.279103074 0.776976702 0.521326980 0.597206041
## [540,] -0.0363129823 0.181706783 0.265574668 0.957983228 0.379320025
## [541,] 0.2019689295 0.875637872 0.225109578 0.573276461 0.403001688
## [542,] 2.4420480751 2.895851023 1.384766013 0.998676871 1.023491559
## [543,] 1.4668537581 1.261661927 2.227989682 0.491051165 1.046840632
## [544,] 0.2700307786 2.143296220 0.781251623 2.659583062 1.276899510
## [545,] 0.1281894571 1.003611702 0.767434124 1.476263280 2.585063293
## [546,] 0.4954587649 0.812427572 0.651116198 0.983717411 0.486377310
## [547,] 1.4042226473 0.702359795 0.313622845 1.066724888 0.799827758
## [548,] 0.8451659681 5.523082962 2.404743210 1.377050580 3.083064852
## [549,] 1.0634708645 0.734012063 0.232526874 1.007031851 0.945602958
## [550,] 1.4108409420 1.636751213 0.681941914 1.376862077 0.759372526
## [551,] 1.2306678619 1.513027039 1.710370070 0.632566822 1.356430023
## [552,] 2.4000061165 2.294203264 0.700934112 1.283697062 1.054354597
## [553,] 1.6334395512 0.956520177 1.397373313 0.438907305 0.523283950
## [554,] 1.5252586117 1.278719724 1.572154417 0.214420365 0.360528212
## [555,] 0.8116642473 0.569024036 0.733437737 2.098933655 0.777533565
## [556,] 0.7908677739 0.639140939 0.529410977 0.021639210 0.537161298
## [557,] 1.4724916250 0.684230923 1.135954117 1.012899084 0.797109374
## [558,] 0.8534801758 1.570853295 1.136653366 0.442246153 1.314486779
## [559,] 1.6140260625 2.232925570 0.286875292 1.891879547 1.011681571
## [560,] -0.1031158746 2.598105506 1.182767157 1.585889279 1.274405234
## [561,] 0.3673178481 1.503437355 0.102981063 1.858427958 1.032750787
## [562,] 0.7122744949 0.397098244 0.577986793 0.035790542 0.384115543
## [563,] 1.0973666785 1.658422449 1.046222984 1.692491299 0.097860182
## [564,] 0.9107346263 1.107249674 0.286234669 2.534264417 1.801197120
## [565,] 0.8194870911 0.954224476 0.360818697 0.401457443 0.257978815
## [566,] 0.7120160969 1.197757650 3.039001790 0.617144511 0.459496492
## [567,] 0.8283656105 0.292218921 0.923033729 0.605855815 0.785154133
## [568,] 2.0730892198 0.530931878 2.096287557 3.292587550 0.984808138
## [569,] 0.2164192056 0.613643047 0.877240963 0.819696116 -0.040966241
## [570,] 0.3959397066 0.992043524 0.941352772 1.125282903 0.681860017
## [571,] 0.5132557278 0.431563907 0.702343705 0.576043685 1.009679362
## [572,] 0.5699804548 0.824723536 2.608663515 1.614676141 1.278692260
## [573,] -0.0057003451 0.179632520 1.202810615 1.014683185 1.002133090
## [574,] 2.2636598344 1.783769359 1.571614354 0.518415657 1.730455729
## [575,] 0.8600937717 0.358296577 1.000618753 1.048755560 0.371686073
## [576,] 0.7992414116 0.429109974 1.336520769 0.535037708 1.110139691
## [577,] 1.0811280239 0.804533041 1.339532815 2.921094839 0.194988710
## [578,] 0.8433742591 0.497239123 0.873239441 2.060299264 0.377956695
## [579,] 1.4873836714 1.500780451 1.143182609 0.467974104 0.554819423
## [580,] 0.4932403006 0.144581419 0.273657585 0.404465004 0.454358045
## [581,] 2.4827713034 2.165294080 0.834995283 0.894716082 0.558936438
## [582,] 1.0623846930 0.963327534 0.412197624 2.758041253 0.288025700
## [583,] 1.0012175919 1.199126403 2.407043650 0.857200911 -0.004171599
## [584,] 0.2142276994 1.510110534 0.415902513 0.219376464 0.170871851
## [585,] 0.6091789917 0.783174717 0.148589791 1.546950009 0.558815596
## [586,] 0.8558434156 0.094290188 0.764461469 0.372312645 1.141057067
## [587,] 1.1016191167 0.163719445 1.320360471 1.739042783 1.125008484
## [588,] 1.6592787654 1.193795813 0.584406848 1.170896509 0.444719568
## [589,] 0.4850471361 1.900826293 0.641916727 0.164214303 2.035883983
## [590,] 1.1034923020 1.339006201 0.665112697 0.831911689 0.457591534
## [591,] 1.7550077466 0.696647505 1.899731575 0.716533386 0.820859974
## [592,] 0.6190457855 1.258421206 0.376604731 2.883186021 0.169152430
## [593,] 0.8544563552 0.418665933 0.591803197 1.310920776 0.501146551
## [594,] 0.7612229114 0.687694755 1.661713190 0.277964325 1.283014367
## [595,] 0.5473463348 1.721185917 0.488168366 0.567449731 1.267000365
## [596,] 2.5911196669 1.087225500 2.239207698 1.525156694 1.689132552
## [597,] 1.4233840621 2.205389856 1.583636634 1.909261256 1.221078066
## [598,] 0.4741080030 0.979096545 0.373828127 1.746253880 1.698177140
## [599,] 0.7061135230 0.986449072 0.450472620 0.780511843 1.267429518
## [600,] 0.7111475172 0.706462476 0.952169757 1.309086643 0.681669755
## [601,] 0.1585539830 1.138629389 1.441338492 0.759755944 2.099820747
## [602,] 0.3372908852 1.079451295 0.355695578 0.963677335 0.731796146
## [603,] 1.1697027923 0.374787466 4.468828871 -0.114154562 1.194550888
## [604,] 0.7997449577 1.366955324 0.916239539 0.542573346 0.495989067
## [605,] 1.4453607368 2.044051697 1.011765818 1.604979974 -0.003865148
## [606,] 2.0329510007 0.971403861 1.159211071 0.538854410 0.877150850
## [607,] 1.9559738324 0.650860694 1.130236756 0.806995129 0.998147581
## [608,] 1.1554569307 0.738761201 1.010991000 2.198516179 0.583239935
## [609,] 1.1718840297 0.523711606 0.681189324 1.716548684 0.469216859
## [610,] 0.3319203136 0.695629902 0.990115124 0.330978340 3.556665771
## [611,] 0.5022036002 0.667374405 0.154333667 0.394418823 0.835113967
## [612,] 0.0674614166 0.070915492 0.031683180 0.309200512 1.099692901
## [613,] 2.1103941825 0.321661129 2.509872140 1.790253889 1.813178055
## [614,] 0.4437510620 0.145648832 0.268726244 0.039685703 0.515026848
## [615,] 0.7705782256 3.406480315 0.428256456 1.084272304 0.318685287
## [616,] 0.3437478799 0.677200254 1.465243285 0.196117538 0.122713907
## [617,] 0.8335610011 0.794129932 0.882136066 0.417200207 1.040317535
## [618,] 1.5493110082 2.245187723 0.265800584 0.440110216 0.717861865
## [619,] 0.5354290819 1.666593499 0.992317793 2.799348817 0.335289926
## [620,] 0.7108859531 2.275240918 1.120721404 1.810775736 0.335191801
## [621,] 1.9390599564 1.791264473 1.162882280 0.652558460 0.663720244
## [622,] 0.6641657811 0.913190895 0.310721622 1.345890539 0.183888559
## [623,] 2.7463817184 0.149579580 0.706208298 1.689248926 0.776921515
## [624,] 1.3104906736 1.418205389 2.892980079 0.073305023 0.557054099
## [625,] 1.6095672486 1.902029742 2.021906975 1.457078856 0.730562068
## [626,] 2.3834257904 0.900347910 0.651100358 1.087916621 0.824289929
## [627,] 0.7067984833 0.445776384 1.016585938 0.626009443 1.115849601
## [628,] 0.1620571708 0.519946690 0.145667100 0.779839809 0.466403588
## [629,] 1.0113055740 0.847049255 1.388530118 0.036960314 0.829275600
## [630,] 0.4306068252 0.945990422 0.744636536 0.865181157 2.738993663
## [631,] 0.2914551723 1.772441638 2.094757296 0.626605765 1.366552964
## [632,] 1.2277085022 0.071391894 0.196704541 1.207900659 3.960077076
## [633,] 3.9054624138 1.708126132 0.719126498 1.514566567 0.591290881
## [634,] 0.3450810646 3.752554502 0.517878291 1.066745551 0.334681475
## [635,] 0.9564948624 0.554726348 0.479775774 0.584308157 0.638517090
## [636,] 1.6442780421 0.319496958 0.313719323 0.375718536 0.449735900
## [637,] 0.4727919715 2.654897259 0.247354274 1.551849398 0.112605303
## [638,] 0.2287243268 0.678675540 1.090847652 1.059421585 1.228426794
## [639,] 0.3680019152 0.828469415 0.245933128 0.392102430 0.029989583
## [640,] 0.3784098989 0.217530612 0.489617331 2.061552857 0.867598941
## [641,] 2.6042171863 0.810548109 0.315585419 0.180404305 0.327803066
## [642,] 0.0504409698 1.378920582 1.155320643 0.048379139 0.890436789
## [643,] 0.7189621206 1.145052699 1.717363728 1.247984785 2.020200615
## [644,] 0.9646387952 0.901899728 1.903495352 0.710593729 1.019056309
## [645,] 0.8564946338 0.230715933 1.620639978 1.229096808 1.751631890
## [646,] 2.0061891885 0.649726935 0.301533503 0.164174306 1.653570157
## [647,] 2.1384075818 2.039418404 0.997551899 0.910927761 1.241639316
## [648,] 0.8592357174 0.336652385 0.558360988 0.465745643 0.094537514
## [649,] 1.6388401501 1.172733274 1.168049314 1.014708295 0.688290916
## [650,] 0.3909373082 2.156736629 0.233574090 0.966699833 1.342182545
## [651,] 2.1067828761 2.160969160 1.443575118 0.440052478 3.771151296
## [652,] 0.3958963097 3.221285419 1.906786312 1.253378694 0.081166307
## [653,] 0.1569555722 0.547271758 2.225036800 1.075629849 0.425725752
## [654,] 0.4947606340 0.365371916 0.833981214 2.325541656 0.211070645
## [655,] 1.4042129079 1.036033356 0.270114297 1.468922425 0.759714983
## [656,] 0.1802105244 1.354093546 2.069646249 0.544562840 1.709869038
## [657,] 1.4670005377 0.866440404 0.273500579 0.430327978 0.637296584
## [658,] 1.1265256215 0.969746391 1.379463310 0.388077806 0.404460439
## [659,] 1.6127872842 2.905614561 0.850159795 1.280914362 0.284646111
## [660,] 0.5044504767 1.336340853 0.152398273 0.306193842 0.808127085
## [661,] 0.4154071207 2.278140322 -0.088174111 0.750838098 0.557876609
## [662,] 3.1264000128 0.440068072 0.341951306 1.069138043 2.884496148
## [663,] -0.0292876178 0.589505385 0.199177054 1.286604471 0.813619642
## [664,] 0.5738326871 0.623012356 0.750555558 1.009269883 1.433394378
## [665,] 0.3492919006 0.453048770 0.188023927 -0.066382888 0.639448084
## [666,] 1.4837607849 0.957177516 0.374657050 0.598640242 0.320067216
## [667,] 0.8793416598 0.985360869 0.672217148 1.060402757 0.997817020
## [668,] 1.9716111256 3.651704106 0.802063814 1.350102744 1.194630750
## [669,] 1.0801564371 0.346609511 0.745337814 0.838050776 0.455966884
## [670,] 0.6612525143 0.312891452 1.652802486 1.224440965 2.256425617
## [671,] 2.3665696624 0.909522901 0.321586608 0.471098869 0.970111520
## [672,] 0.5405645338 0.010976401 0.937864576 1.519332652 1.730627385
## [673,] 0.1102504485 0.251033372 0.374192225 0.482889117 2.153515879
## [674,] 0.9731456280 0.889933831 1.792546179 1.001685490 0.286758801
## [675,] 1.6341424068 0.651073077 2.200941240 -0.101415688 1.145026886
## [676,] 1.3005419012 1.750784714 1.002548509 1.036225822 0.199809872
## [677,] 0.3278153333 1.027533836 0.486375034 0.824453097 0.184722565
## [678,] 1.3433443688 1.079086237 1.845832103 2.138328628 -0.110753485
## [679,] 0.4833430885 0.524733194 0.385101535 2.511148219 3.414226892
## [680,] 0.6817627865 0.929619479 1.205857415 2.220422159 0.873657176
## [681,] 0.6816987330 0.769335531 0.011954681 0.713494120 1.029496119
## [682,] 2.5014354187 1.215723422 1.337060744 1.417244915 1.298396058
## [683,] 0.2369194054 1.252479725 0.245156064 0.737835342 1.157697936
## [684,] 2.2814677649 0.769883766 1.880589586 0.352491903 2.108816844
## [685,] 1.1573292978 1.204165436 0.716697688 0.174745089 0.387914292
## [686,] 0.8580740045 1.203836741 1.627572670 0.292925255 1.320548248
## [687,] 1.3411921587 1.310736003 0.478786984 1.407684790 1.615822744
## [688,] 0.7797890343 0.504946533 0.831128162 0.049694313 0.823702902
## [689,] 2.0034143073 0.581170095 0.097270231 2.042365143 -0.217175080
## [690,] 1.2586041368 1.190099785 0.063931219 0.797821491 1.326351661
## [691,] 1.2278358985 0.458633252 2.759040433 1.657379864 0.344079670
## [692,] 0.4831836379 0.407301843 -0.008391736 1.446589029 0.244652892
## [693,] 1.5669534273 1.254118149 1.427864317 1.078123396 1.224149100
## [694,] 0.7177198107 0.700348764 1.807746121 0.677371648 0.690493516
## [695,] 1.8287325483 0.371997536 0.929456430 2.510082561 0.783095416
## [696,] 1.5689703838 1.348714843 1.598488657 1.883659993 0.628558902
## [697,] 0.8998225317 0.405418899 0.276647558 0.880347886 0.341027895
## [698,] 1.9296478721 0.992981336 0.574586234 0.188267412 0.484053346
## [699,] 0.4110339047 0.995653255 0.855443012 1.278064043 1.993389270
## [700,] 0.4525450530 0.833889375 0.190621981 1.384704944 1.467452141
## [701,] 1.5079023620 0.882681056 0.135981731 0.819689272 0.427907692
## [702,] 1.4230512255 0.557654740 1.001788412 1.080348393 1.166521247
## [703,] 0.6944946463 0.894928745 1.085612605 1.093530494 0.947067438
## [704,] 1.1070399777 0.823416178 1.118446486 0.415954570 1.458413372
## [705,] 1.7117805650 0.608787817 0.996357390 0.835148469 1.910032361
## [706,] 1.0922363185 0.683620917 0.417674477 1.369768195 0.649033712
## [707,] 2.0093622078 0.349004524 0.895719120 1.438484730 0.711936531
## [708,] 0.8960897785 0.610947175 1.339962890 1.299966695 0.245909792
## [709,] 1.0354973589 0.766599751 1.024467940 0.318195973 0.712189374
## [710,] 0.3126788495 1.251339881 1.458234698 0.952418763 0.214415294
## [711,] 1.4393797996 0.528357102 0.576872183 0.362223964 1.317373578
## [712,] 0.3983139743 0.462462929 1.784733502 1.422804339 1.579070923
## [713,] 1.1295257842 0.452236498 0.566847724 0.020558448 0.459990230
## [714,] 0.9337698295 0.751894171 0.261671576 0.460855055 0.939974144
## [715,] 2.7125837270 0.879683510 0.154498324 0.998546228 1.128361625
## [716,] 0.9832347627 1.221201350 0.604002582 0.532347506 1.235936787
## [717,] 1.2073781176 2.291197584 0.652600926 0.441423128 1.491416994
## [718,] 1.1488504638 0.176732041 0.653357328 0.053416565 0.090032334
## [719,] 0.8015541591 0.976920053 1.620257074 0.562171661 2.265087027
## [720,] 0.9537895957 2.561818378 3.166330069 0.999347388 0.703454600
## [721,] 1.3082923553 1.654460951 0.934779828 0.390163781 1.187124154
## [722,] 0.2501744957 2.566549805 1.160396946 0.921867171 2.108404353
## [723,] 0.2403173191 0.521709384 2.079033604 1.427114562 0.918296402
## [724,] 0.9229794169 0.545578837 2.510029430 0.727119916 0.260788929
## [725,] 0.1543077424 0.236389255 0.235943726 1.765099287 1.892000006
## [726,] 0.5514852262 0.449145844 0.848055963 0.942427683 0.790053723
## [727,] 0.2115329962 2.119891845 0.250616240 1.562830826 0.143198895
## [728,] 2.4976513521 0.610233015 1.514247632 0.132443776 0.385302563
## [729,] 0.5840777690 0.941342725 1.256210224 0.714432731 0.574346200
## [730,] 0.3930202564 0.382591264 0.783143637 1.476220715 3.102546928
## [731,] 0.4056728935 1.605685431 0.856967887 0.186655229 2.009452610
## [732,] 1.4471985157 1.250674198 0.774084387 1.732646214 2.757197344
## [733,] 0.4516170960 2.005463042 0.921323697 1.800877960 0.645132815
## [734,] 1.3184109275 1.515848811 0.839820953 1.001532182 2.842636901
## [735,] 1.8686627040 0.440493799 0.706611684 1.185854473 2.931724649
## [736,] 1.4393289215 1.625828559 1.687340667 1.024348629 1.052253072
## [737,] 0.4292300218 0.125377325 0.474200237 0.324475682 0.481283583
## [738,] 0.7003880787 0.283753667 0.674767197 1.771814297 0.332293567
## [739,] 0.2959528877 0.877930873 0.797338550 0.731434524 0.503775171
## [740,] 0.6167053098 0.357963331 1.183886887 0.345569232 0.373203188
## [741,] 0.7826802434 0.958498579 0.284371454 0.968634718 1.130591475
## [742,] 0.0078082079 0.746744395 1.585713210 1.483319249 1.185972240
## [743,] 2.9066020930 1.733585722 0.953941899 0.452462588 0.865389060
## [744,] 1.7483101947 2.376369171 1.700839792 0.643087667 1.981222167
## [745,] 1.0813088067 0.610408271 0.566992774 0.943736122 0.461570949
## [746,] 2.9303694586 2.248525309 3.317869088 0.771070391 0.179546996
## [747,] 0.1859961386 1.770376418 2.320121857 1.547928928 0.416288810
## [748,] 0.7352184131 0.848308496 0.146638746 0.887463383 0.865366606
## [749,] 0.2612471973 0.834082252 0.129968560 0.973047249 2.586836827
## [750,] 0.7805410797 1.008321082 2.066049646 0.416567348 1.729381714
## [751,] 2.5695079403 0.473113364 2.162002493 0.510154067 0.595815135
## [752,] 1.6167092624 0.861033502 0.050010404 0.362359553 1.242429902
## [753,] 0.9991223646 0.403529133 0.855144925 0.297357886 0.679366577
## [754,] 1.3977280539 1.082427170 0.652848364 1.038262805 -0.080043554
## [755,] 0.4196802064 0.257278682 0.401685929 1.577401507 1.815405975
## [756,] -0.0395643307 0.819560967 1.261963866 0.969631693 0.910389304
## [757,] 0.3849346455 1.556849828 0.518990048 1.603103909 0.427750712
## [758,] 1.7541782018 1.188482800 0.264167983 0.660384669 1.304728066
## [759,] 1.1641375017 0.846600582 0.639623351 1.406025473 0.717849574
## [760,] 0.8121430784 1.803733450 0.786072020 0.992790354 1.002888270
## [761,] 0.7667013064 0.310913841 0.347710064 0.687485301 0.129699988
## [762,] 0.7801750041 0.379542653 0.567418014 0.578628071 0.704258322
## [763,] 1.1638407792 0.134581145 0.877216733 0.837083513 0.274687231
## [764,] 0.4061153171 1.619100967 0.266968745 0.584528856 0.636571488
## [765,] 0.3782654681 1.027153629 0.784125097 2.531020559 1.529500886
## [766,] 1.1744364432 0.745736774 0.447862752 1.489193228 1.466269154
## [767,] -0.0645346733 2.330588223 1.178341288 2.788745183 0.460144094
## [768,] 0.5760547036 3.152982222 2.876281888 1.827378937 0.635425761
## [769,] 1.6994982119 0.762109331 0.632020781 0.802296682 0.505485866
## [770,] 0.1361481521 1.146306036 0.201586700 0.743040460 0.319014085
## [771,] 2.5123475690 0.521280514 0.291824340 1.800762492 0.136285959
## [772,] 1.1244993698 3.766533284 1.706596942 1.213544838 0.582158779
## [773,] 1.0661812111 0.279674588 1.197799445 0.806235516 0.993548179
## [774,] 4.0851041664 0.904974943 1.940240176 1.151520232 1.210925799
## [775,] 2.2200647679 1.890321554 0.929815521 0.151613053 0.801325627
## [776,] 1.2564557175 0.052230334 0.167275055 0.803300581 0.364304319
## [777,] 0.2481686320 1.056481798 1.639080800 0.406036062 0.846065158
## [778,] 1.0478500152 0.173529172 2.007778275 0.434188278 0.795598404
## [779,] 2.8737915156 1.909323009 0.331442209 3.536880422 0.652403259
## [780,] 1.5477473444 0.384094386 0.811324336 0.069965959 0.809353380
## [781,] 0.6761051937 0.139008658 1.683138951 0.419515068 0.365331352
## [782,] 0.1128518244 0.106464774 0.662870484 0.261035049 1.457031896
## [783,] 0.7650004389 0.657963338 0.626259876 2.311907562 1.470874655
## [784,] 1.9970719625 -0.091910671 2.300226218 2.220093206 3.507659993
## [785,] 0.7940707699 0.343122652 1.066094431 0.805920464 0.818716137
## [786,] 0.8960241348 1.756770082 3.793897807 1.630754557 0.581788720
## [787,] 1.5249049331 1.521362849 0.849175252 0.726501998 0.262771053
## [788,] -0.0255629711 2.030909965 1.907423241 0.851876517 0.804722321
## [789,] 1.3966482007 0.682551640 1.377467881 0.861545898 0.935494157
## [790,] 1.2493703713 0.189601214 0.911409332 1.053631471 0.669224429
## [791,] 0.3046979636 1.352584885 0.271273593 0.239862083 0.170947739
## [792,] 0.4643290011 0.519527608 0.455725991 0.072826900 1.634868830
## [793,] 0.8401367813 1.756636879 0.573776062 0.691935913 3.072885290
## [794,] 1.7655131816 0.805461509 0.278186786 0.845896689 1.505477784
## [795,] 0.6598945076 0.717407340 1.085712461 2.350876818 0.716789794
## [796,] 0.9243352178 0.780286410 0.156026201 1.184221624 0.510475387
## [797,] 0.5317140019 1.711115793 0.490262839 2.415592922 1.136480834
## [798,] 3.0409126241 1.656225858 0.670685253 0.736828384 2.330239067
## [799,] 1.4989836822 0.880767133 1.042667643 1.079417904 0.761967344
## [800,] 1.2981859250 0.423895128 0.373596780 -0.013047757 0.640278423
## [801,] 0.2810393776 1.472246611 0.831692662 1.063752404 0.983497937
## [802,] -0.0004136254 2.798462018 0.863649672 2.429504790 0.801670843
## [803,] 0.9517629957 0.423931646 1.792154718 1.402056701 0.873679448
## [804,] 1.1594620491 0.801329090 3.502169263 2.303276501 2.522215629
## [805,] 1.5656275105 1.642064926 0.878280576 0.358760739 0.336575778
## [806,] 0.4213375808 0.823199475 1.554549847 0.807815444 1.804643273
## [807,] 1.0145491543 1.422268752 0.525650952 1.343513890 1.631319160
## [808,] 4.7969425462 0.975190319 1.224474761 0.868103438 0.612783117
## [809,] 0.7603446160 1.188632674 0.327558404 0.191883400 0.322844599
## [810,] 1.0329656927 1.842706654 1.243269104 0.467883724 0.650794065
## [811,] 2.0275375372 0.675259178 2.158436695 0.971331857 0.749504158
## [812,] 1.3816986181 0.818651370 0.032079230 0.920854125 0.454053356
## [813,] 1.0212277236 0.303389021 2.917440985 0.604947552 0.775615828
## [814,] 1.2638534333 1.097881140 1.244657854 0.614990591 1.233006737
## [815,] 0.1571950728 2.090048427 1.389087779 0.236260471 0.913436552
## [816,] 0.6295440203 0.007309081 0.607215336 0.609960856 0.094343795
## [817,] 1.1572463636 0.098276971 0.102854210 0.353320219 1.181930272
## [818,] 0.2108290903 0.796369904 0.404304156 0.908473710 0.158366038
## [819,] 0.9426036334 1.055140637 1.135151301 0.895066382 2.847487471
## [820,] 0.8547362747 1.824275157 0.356172331 1.851866454 1.288761361
## [821,] 0.3608330171 0.295514345 0.714028559 0.245340885 0.353914553
## [822,] 0.9578516845 2.053663850 2.197976939 0.623182218 0.744128804
## [823,] 1.4159298149 0.866011999 1.281440257 1.592963407 0.256251162
## [824,] 0.4034256257 0.569122426 0.533896281 1.680629803 0.823226443
## [825,] 0.7051911371 0.163213838 1.011775635 0.446686139 1.093554134
## [826,] 1.3738597446 0.417742638 1.842738147 0.972101142 0.067958640
## [827,] 0.6291394771 1.016155963 1.174584640 1.088698621 1.495648455
## [828,] 1.3371361922 1.111729379 0.463421550 0.791782282 0.781023243
## [829,] 0.6587814373 1.642217628 2.437860045 0.829123600 1.492062188
## [830,] 0.2160765738 0.845688745 1.900682888 1.533839832 0.214976551
## [831,] 2.8333564187 0.803699470 0.955262928 1.374324191 2.394286475
## [832,] 0.4729640873 0.752438142 0.726110591 0.909883634 1.401523861
## [833,] 2.0350129870 0.955743306 0.192017328 0.871362588 0.698041051
## [834,] 1.3161329263 0.276727292 0.582969678 1.390073853 4.022103038
## [835,] 0.8933036050 0.641575397 0.982463239 0.180207394 1.328717888
## [836,] 1.5524032735 1.140408723 0.139110586 1.290176789 1.957659316
## [837,] 0.7011674669 0.194346528 0.449561866 1.599005250 0.583761955
## [838,] 1.1721515329 0.342349963 0.709482646 0.572498187 0.712914609
## [839,] 1.5969784584 1.361142959 0.766739649 1.497270078 2.121898923
## [840,] 1.3994486999 0.398190139 1.529198227 0.555048867 0.470192999
## [841,] 1.4713658103 0.637836816 1.023645290 0.285028261 1.313335689
## [842,] 0.4562303007 0.467870223 1.335315363 1.381739051 1.162934461
## [843,] 1.1813672666 0.410404533 1.021055767 1.129962107 0.194681813
## [844,] 0.2769824363 0.995692325 0.845327339 1.173013734 0.170869428
## [845,] 1.0384313478 0.795373467 1.550164965 1.269681965 0.420450922
## [846,] 0.3101085293 1.152742397 1.598845305 1.197514526 0.647780694
## [847,] 1.5188166532 2.847539418 0.856485126 0.705489892 1.048959031
## [848,] 2.6937032193 0.342951456 3.394146870 1.441030492 1.466797853
## [849,] 1.4448849560 1.149832625 1.618292509 0.555333240 1.615096926
## [850,] 1.0065833054 0.997968733 0.748712475 1.505246901 1.844081424
## [851,] 0.3461451264 0.605785464 3.534321296 2.023453969 0.679281547
## [852,] 1.4345957612 2.379308218 1.371393291 0.832437880 0.531388581
## [853,] 1.6591141415 0.873031471 0.392706627 0.659180159 0.796361516
## [854,] 0.0980450073 0.732598894 0.670053622 1.193887925 0.610504042
## [855,] 1.2358118821 0.949704872 1.217985034 1.858715756 1.921480167
## [856,] 0.2493727160 0.276041379 0.480369550 0.269636129 0.617919755
## [857,] 1.3263984380 0.907118133 1.498593247 0.553477558 0.733005609
## [858,] 1.9398322999 1.395794300 0.082628268 1.522234939 0.755236064
## [859,] 0.0302511551 1.493982706 1.429679268 1.190416393 0.227426422
## [860,] 0.1717939757 0.345742503 0.339773555 0.320653883 0.878721627
## [861,] 1.2282241338 0.986052293 0.199353908 0.856209682 1.580266599
## [862,] 1.3073583115 0.326011589 0.910079938 1.754624542 0.336068545
## [863,] 0.8944579250 1.203566446 0.368831563 3.203623355 0.827672247
## [864,] 0.2943048228 2.379036333 0.324486623 1.486193734 0.825160572
## [865,] 1.1926479050 1.979562848 0.861009731 0.689092680 0.142215377
## [866,] 1.9747843280 0.881089569 0.550938297 1.375343973 0.581397727
## [867,] 1.3624230937 1.662009221 1.130187391 1.189382752 0.660521100
## [868,] 0.2736299267 0.338694752 0.258557532 0.180246269 0.565818003
## [869,] 0.4458673745 0.182790084 0.965702220 0.393115792 1.695590663
## [870,] 0.5223137841 0.672991404 0.352282903 3.422313940 0.003859494
## [871,] 0.2206042046 1.564773577 2.159979900 0.823342720 1.296793549
## [872,] 0.4056403823 0.960858322 0.582028599 1.542124650 0.364559839
## [873,] 1.3155376951 1.276201414 0.547281494 0.534430820 0.887591046
## [874,] 0.7049403619 0.675937396 0.599413140 1.908318663 0.727683012
## [875,] 1.8901537792 1.511312029 0.777297766 -0.082702011 0.804671380
## [876,] 0.0589728930 1.528051822 0.889175765 0.255927910 0.565940572
## [877,] 0.6739287113 0.512823658 1.103124880 0.777528187 1.889742303
## [878,] 0.7034571196 0.969451684 0.471316199 1.418713238 0.040960155
## [879,] 0.4908354829 0.929403804 0.469598564 0.182682734 1.255514762
## [880,] -0.0220345055 1.708954289 0.883860013 1.595214275 0.540626441
## [881,] 2.7363462761 1.727920143 0.900482032 1.017015716 2.098130862
## [882,] 0.4668823424 2.502246258 1.920893494 0.505655418 1.171160741
## [883,] 0.8665883260 0.770155881 1.947507424 0.586910316 0.993653626
## [884,] 0.5340082006 0.582955870 0.350970498 0.005354939 0.449721880
## [885,] 0.4993861095 1.171436872 0.866354986 1.052600848 3.142533983
## [886,] 2.2679138320 1.143497000 0.104914944 2.630959454 1.203796170
## [887,] 0.5469737983 0.520775892 0.990237988 0.401079581 0.870553379
## [888,] 2.0676325514 0.433223596 1.197390484 0.419259831 0.891119936
## [889,] 0.1768797436 0.251162745 1.755177198 1.389831116 0.029745106
## [890,] 1.8625214559 1.769834533 0.668498292 1.374310828 0.810065299
## [891,] 0.7829891958 0.993391683 3.917717578 1.963754459 2.226860259
## [892,] 2.3353627109 0.884362264 0.453055816 4.613803119 0.811950006
## [893,] 0.5416258935 1.083922771 1.158891272 0.178326218 1.037420580
## [894,] 0.4517867828 1.633456604 0.200262009 1.427239039 1.026904488
## [895,] 0.4364672173 1.640863124 0.927459539 0.776273828 0.603179880
## [896,] 0.7972482147 2.824925361 1.147468269 2.182130306 -0.058057293
## [897,] 0.2333079968 0.892624682 0.403502717 1.494945150 0.927117918
## [898,] 2.0446753314 0.615732284 1.184824245 1.954243747 3.960286978
## [899,] 0.6036323685 1.020388655 1.177312224 0.974664733 0.276152660
## [900,] 0.8685923101 1.480330268 1.605519343 0.354756471 1.113189662
## [901,] 0.7756347907 0.706854590 0.483602253 1.233984077 1.694020197
## [902,] 0.5514464724 0.540603297 1.041994015 0.520688631 2.247262268
## [903,] 0.8128852437 0.563754347 1.031634707 0.706177616 0.674352275
## [904,] 1.0447550454 1.489606085 0.940096069 1.166252880 1.479980377
## [905,] 0.2229589330 0.345509482 0.912886833 1.895347039 0.493707475
## [906,] 1.7015672686 0.279332805 1.897877072 1.673766565 0.927032296
## [907,] 0.9046979119 1.858025855 -0.054419602 0.232157206 0.655375170
## [908,] 1.4480858691 1.515773035 0.901692989 1.253162771 0.408312106
## [909,] 0.7772875248 -0.014047522 0.385436159 0.479963844 0.188872364
## [910,] 0.2168397635 1.649656571 0.796722450 0.243918961 0.514341451
## [911,] 2.0288146484 1.883438493 -0.054687026 1.271388611 1.517546800
## [912,] 0.5814565420 0.699459805 2.412018066 0.625013899 0.802336905
## [913,] 0.5487160081 2.074309525 0.368972852 2.730649580 0.831309231
## [914,] 0.8844160921 1.006296133 0.394295643 1.612424427 0.542606193
## [915,] 1.6659152969 0.863549289 2.053024344 0.443875904 0.426826391
## [916,] 0.9441063428 1.422667817 2.717157823 1.008862123 0.301807697
## [917,] 1.1110925328 1.576368465 0.426080633 0.815162717 2.505342155
## [918,] 0.2324531573 0.864194385 0.511765145 2.209715732 0.746080579
## [919,] 3.4631439781 1.044004607 0.564554698 1.520938551 1.008414208
## [920,] 0.3739813630 0.935509074 0.555951645 0.115331828 0.616368616
## [921,] 0.5551040279 0.380190662 2.430881310 1.514002297 0.256408051
## [922,] 0.8368854804 1.664865700 0.788971147 0.331888747 0.353472875
## [923,] 1.1283472558 0.382274760 0.619205623 2.026255215 2.005202764
## [924,] 0.8740213691 0.157428974 0.861780643 0.711795489 0.676334935
## [925,] 0.3262900254 1.468089548 1.240330788 1.222214116 0.249961148
## [926,] 0.6875137370 0.487589900 0.702102258 0.438370318 1.424170209
## [927,] 1.1811769913 0.836186740 1.216435967 1.469473044 1.483477177
## [928,] 2.5391295160 0.233187861 0.062755166 0.213510397 1.244216862
## [929,] 0.8560526733 0.561794311 0.228876491 0.669268916 2.971263209
## [930,] 1.7459757037 1.023082427 1.388180645 0.377878600 1.183254090
## [931,] 1.1379542064 0.860167144 0.417877876 1.300879122 2.898852774
## [932,] 1.3948831199 0.832058382 1.881137789 1.269132240 0.719152858
## [933,] 0.4691290793 0.781254172 0.678491094 0.165908953 0.822705059
## [934,] 0.1130011398 0.266599844 0.068354514 0.608538662 1.033414388
## [935,] 2.3213114692 2.768777737 1.038119944 1.976698549 0.782717588
## [936,] 1.4385936529 1.322774717 0.015515807 0.882537666 0.565505966
## [937,] 0.6044621875 1.454150020 4.837486642 1.235237251 0.246740026
## [938,] 0.6424675243 1.093014413 1.101405961 0.555240939 2.149811608
## [939,] 0.7107859366 1.786123136 0.742420226 1.849893017 1.605305274
## [940,] 0.7248188781 0.210904091 -0.015945875 1.066310055 0.777047722
## [941,] 1.2644826188 1.506853200 0.556622657 0.326498940 0.692196830
## [942,] 1.4857983406 0.764968686 1.323853442 1.263467336 0.443304951
## [943,] 0.0090491224 0.321722307 2.079597605 0.450114552 1.618866990
## [944,] 1.0422690829 -0.033276139 1.309362352 0.586265731 0.680570242
## [945,] 0.4021798725 1.365485613 1.285444882 1.643800009 0.801506440
## [946,] 0.2301819439 0.463253955 0.410170203 0.924074222 1.748804483
## [947,] 1.0418809511 2.297518136 1.102962606 1.277603756 2.497220721
## [948,] 1.3821464314 0.269413658 0.242275914 0.462359285 1.639543840
## [949,] 1.2838182072 1.993664038 -0.012638752 0.474388464 2.044933780
## [950,] 0.8502660785 0.338758413 0.802330164 0.613120691 0.927887054
## [951,] 1.1964912055 1.876050764 0.897471127 0.313610245 3.937532913
## [952,] 0.3765117980 1.162315065 1.487792086 2.256807441 0.571652432
## [953,] 0.4876643514 0.781772459 0.698292364 0.707920696 -0.024310853
## [954,] 0.9053378671 1.038773443 1.174943286 0.826416904 0.938518639
## [955,] 0.8299497785 0.391238551 1.246066391 0.294353233 0.894318793
## [956,] 0.7683556253 0.778570557 0.368033048 0.592261897 1.220917017
## [957,] 0.6078602734 0.736419740 0.764456074 2.023054017 0.887369223
## [958,] 0.1804438493 3.004026432 1.208654878 0.584428925 0.526545796
## [959,] 1.1091327665 0.533602031 0.419272786 0.213999194 0.508001243
## [960,] 2.0379637663 1.756765635 0.466229009 1.760804377 1.254338506
## [961,] 1.2645054157 0.631814896 1.293433175 0.568534532 0.961909903
## [962,] -0.0097940260 0.918054881 1.352597380 1.692128545 0.627702044
## [963,] 3.2328999013 1.382085842 1.230778215 0.441409980 1.342588674
## [964,] 0.4559427087 0.133405069 0.956609462 0.916229341 0.385883336
## [965,] 0.8291204229 1.198156529 0.602216025 0.989335827 1.965043732
## [966,] 1.3924030393 0.624267801 1.687824607 0.679671471 0.873987539
## [967,] 0.8345209764 1.294866505 0.284222451 1.559821968 1.956058987
## [968,] 0.3047561798 2.249121383 0.709452702 0.941796457 0.375388615
## [969,] 0.7115488511 2.174386880 1.313420364 0.849494147 1.980492325
## [970,] 1.3397812728 1.607947767 0.836141251 0.579921022 0.972109718
## [971,] 1.3005124709 0.680174210 0.919771175 1.250046870 1.581487752
## [972,] 1.1361184967 0.264588333 0.287584652 1.446615262 1.145654519
## [973,] 0.8977994123 0.608882236 0.675902664 0.700752480 0.978587281
## [974,] 0.2305402631 0.398538632 0.675426584 0.261719746 2.148028123
## [975,] 0.5766167462 0.284817202 0.520760507 0.208984512 3.869575689
## [976,] 1.4550858499 0.122226538 0.243587171 0.769625543 0.604347486
## [977,] 0.4387556308 1.477416502 0.105756181 0.855190469 0.695554996
## [978,] 1.5833339425 1.845313846 0.850146370 0.691556090 0.770683025
## [979,] 1.3749061139 1.541154780 3.831982258 1.732537582 1.380990070
## [980,] 0.5067873716 0.434197885 0.566114697 1.546454471 0.310352318
## [981,] 0.5831818543 0.676159955 0.523583514 -0.147559327 0.474272996
## [982,] 0.9383350998 0.927203699 0.389847317 0.832548207 4.443328531
## [983,] 0.4053273672 2.638485603 0.590127573 1.035634054 1.384822823
## [984,] 0.8508407420 1.766818062 1.675912990 1.461822507 1.512798202
## [985,] 0.8259495369 1.497919129 3.888632365 0.849156970 0.627463864
## [986,] 0.5102302001 0.089099755 1.917017980 0.688806027 0.019194523
## [987,] 0.4419885695 0.297171601 0.938056009 1.062461352 0.067224923
## [988,] 0.0528003542 0.510996788 0.488050675 0.799531701 2.007196746
## [989,] 2.5478685253 0.062152069 1.043761460 4.281392039 0.800580101
## [990,] 0.5617999695 0.475682328 1.296746000 0.371885098 0.302909175
## [991,] -0.0587552018 0.606824885 1.019296213 0.366943799 1.359423667
## [992,] 0.0753971122 0.647206900 1.268956432 1.295225416 1.405413307
## [993,] 0.6718777226 0.133710114 2.781265260 1.467457680 0.973085238
## [994,] 0.2688203443 0.279583310 1.319046277 0.723372724 1.029379828
## [995,] 0.6797800086 2.193864007 0.611698975 0.058411296 0.480969608
## [996,] 0.4711768366 0.931722397 2.932160662 0.458603182 4.701597535
## [997,] 1.1344037054 1.193878824 1.081297421 3.098590611 0.352833441
## [998,] 0.8650671105 0.515233477 1.590938775 1.809711943 4.012287788
## [999,] 0.6615174877 1.043421630 0.822249590 0.297618364 1.011869133
## [,11] [,12]
## [1,] 0.209518456 2.879874269
## [2,] 0.882828286 0.644752455
## [3,] 1.753095484 1.165755146
## [4,] 2.115494158 2.490619313
## [5,] 0.364190114 0.363430046
## [6,] 1.091433076 1.574416426
## [7,] 0.586266846 0.044066150
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## [9,] 1.887628978 1.383687810
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## [13,] 1.281904254 0.426904463
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## [220,] 2.425650385 1.267931774
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## [230,] 0.695749404 1.718335040
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## [232,] 1.813030235 2.341285163
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## [234,] 1.112476585 0.859214397
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## [238,] 2.148134148 0.733022806
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## [248,] 1.598347805 1.275254602
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## [250,] 0.758873559 0.793210203
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## [683,] 0.606494930 0.962104973
## [684,] 0.036836435 0.613350481
## [685,] 0.916256840 0.253435004
## [686,] 2.394745597 1.193290051
## [687,] 1.063963690 0.822789303
## [688,] 0.184372333 1.184572396
## [689,] 0.385047740 1.287679986
## [690,] 2.266484834 0.089472212
## [691,] 4.739018782 1.130987198
## [692,] 0.377394644 0.614339382
## [693,] 1.163766203 0.553313126
## [694,] 0.591669420 0.834717892
## [695,] 0.844726826 0.698669819
## [696,] 0.290955405 2.548558759
## [697,] 1.189749406 0.408856897
## [698,] 0.622349439 0.426479321
## [699,] 1.081910531 0.859027855
## [700,] 1.960761555 0.258402702
## [701,] 0.421414542 0.366481273
## [702,] 0.379997029 1.021830014
## [703,] 0.689823559 0.844443878
## [704,] 0.516144830 1.242944127
## [705,] 0.429214940 2.079870770
## [706,] 0.651843789 0.643498176
## [707,] 0.130665615 0.305895147
## [708,] 0.931863557 0.148864896
## [709,] 0.705952228 3.753734867
## [710,] 0.787988026 0.449773812
## [711,] -0.092650435 1.622756364
## [712,] 0.423066873 1.210459830
## [713,] 0.176813539 0.515858396
## [714,] 1.823117681 1.800476101
## [715,] 0.658496446 1.312194880
## [716,] 0.357458130 2.284156515
## [717,] 3.825015632 0.501910570
## [718,] 2.682318783 0.441920610
## [719,] 0.617635263 0.613844922
## [720,] 0.838754557 0.429821527
## [721,] 0.939916068 0.908934101
## [722,] 0.547508769 1.700992396
## [723,] 0.653349047 0.477717384
## [724,] 0.093994956 0.820487806
## [725,] 1.052614757 0.212938307
## [726,] 0.950432850 1.567225075
## [727,] 0.457413907 0.386388114
## [728,] 1.629616479 1.229037469
## [729,] 0.627846481 1.935563724
## [730,] 1.409005640 1.014544717
## [731,] 0.153604885 1.004482583
## [732,] 1.040959814 1.687854840
## [733,] 1.280143626 1.697925298
## [734,] 1.620785521 0.187308772
## [735,] 1.663749513 1.308567722
## [736,] 1.005962845 1.065556953
## [737,] 0.434122529 2.026511038
## [738,] 0.904222889 0.580309453
## [739,] 0.336378624 0.119863304
## [740,] 0.057909537 1.213309409
## [741,] 0.943154139 0.523643427
## [742,] 0.871929549 2.076492765
## [743,] 0.173108877 1.018863024
## [744,] 0.355124583 0.274022632
## [745,] 0.905011759 1.587366575
## [746,] 1.571175374 1.414877300
## [747,] 0.655873011 0.268036972
## [748,] 0.934459572 1.256009489
## [749,] 1.300542222 0.679802907
## [750,] 1.118778704 0.230281237
## [751,] 1.186339827 1.884239564
## [752,] 0.958851346 0.498838657
## [753,] 0.588578338 0.995544613
## [754,] 0.192945532 0.343091968
## [755,] 0.121725102 0.358112051
## [756,] 0.130345623 0.719478648
## [757,] 0.030222656 1.093562976
## [758,] 1.324590437 1.554797909
## [759,] 0.866944717 0.583670753
## [760,] 0.520085827 1.588841330
## [761,] 3.665451017 0.360326886
## [762,] 0.313866567 0.180822336
## [763,] 0.613986186 0.532271239
## [764,] 0.612250619 1.418105405
## [765,] 0.545357708 0.739616443
## [766,] 5.265615179 0.496734653
## [767,] 0.569129697 0.513388641
## [768,] 1.165893684 1.090085485
## [769,] 0.298470837 0.720891145
## [770,] 1.835993803 0.618887286
## [771,] 0.357464999 0.653410614
## [772,] 1.236981758 1.174890750
## [773,] 3.221866200 0.710795474
## [774,] 1.532152537 2.099271925
## [775,] 0.272519288 1.171454959
## [776,] 0.657904766 0.190480100
## [777,] 1.490675248 0.360027959
## [778,] 0.559131878 0.722527828
## [779,] 1.910385994 1.276351552
## [780,] 1.268721409 1.128199292
## [781,] 0.840797768 1.460765003
## [782,] 0.509637670 1.542108581
## [783,] 1.946232670 0.646227201
## [784,] 2.662654667 0.488690937
## [785,] 2.314572242 0.462068655
## [786,] 2.460201512 0.527555270
## [787,] 0.938634796 0.514515216
## [788,] 1.274493224 0.199390134
## [789,] 0.965643369 0.511066896
## [790,] 0.723106151 1.292675327
## [791,] 0.601874853 1.494998068
## [792,] 0.485518490 0.854425005
## [793,] 1.031038175 0.891830111
## [794,] 0.579356776 0.411799130
## [795,] 0.498745396 0.966114962
## [796,] 1.441475662 0.911639254
## [797,] 1.041678759 0.863510073
## [798,] 0.923497243 1.724701537
## [799,] 1.256143283 0.588728496
## [800,] 0.344979340 3.627891756
## [801,] 0.719582699 0.633363091
## [802,] 0.513713394 1.130623855
## [803,] 1.015066685 0.499789938
## [804,] 0.508929932 1.844337019
## [805,] 0.405642546 1.351853065
## [806,] -0.072933615 2.469186914
## [807,] 1.858457028 1.054250799
## [808,] 0.894433419 1.589274154
## [809,] 1.253274215 0.717658054
## [810,] 1.027980689 0.824428978
## [811,] 2.457963664 1.526672748
## [812,] 3.453410734 0.598283357
## [813,] 0.601544198 1.106631559
## [814,] 1.330145049 2.458139208
## [815,] 0.657134331 0.589421511
## [816,] 1.543285434 0.956707692
## [817,] 1.790945546 1.619933758
## [818,] 0.446828661 0.508080076
## [819,] 1.925963131 3.402076346
## [820,] 0.547964997 1.348139930
## [821,] 0.194203763 0.301922711
## [822,] 0.046143699 1.432644375
## [823,] 0.619767355 0.737501779
## [824,] 0.169656297 0.979627674
## [825,] 0.213212393 0.435876035
## [826,] 0.795488899 1.308051661
## [827,] 0.565133912 0.261483356
## [828,] 0.585787736 0.385713995
## [829,] 2.244619257 1.041620560
## [830,] 0.621450752 0.696028944
## [831,] 1.440441362 0.536254809
## [832,] 1.301582624 0.686753578
## [833,] 1.019520714 0.442833871
## [834,] 0.613420791 1.964403916
## [835,] 0.527923358 1.179490070
## [836,] -0.148892983 2.247062672
## [837,] 0.140940284 0.708043041
## [838,] 0.451402340 1.469388200
## [839,] 0.102922522 0.578212420
## [840,] 0.674611375 0.078958926
## [841,] 1.991470240 1.814318615
## [842,] 4.005892922 0.446693831
## [843,] 1.333874891 1.354381592
## [844,] 0.546592916 0.699300190
## [845,] 0.709229165 0.435869022
## [846,] 2.332431216 0.316761553
## [847,] 2.140610280 1.476065574
## [848,] 0.789023494 1.821330074
## [849,] 0.690295479 0.999123262
## [850,] 0.823208578 1.103939797
## [851,] 1.429630546 0.915316213
## [852,] 0.776816537 1.164670478
## [853,] 2.402339977 1.300763211
## [854,] 1.207512049 2.605477184
## [855,] 0.354014386 1.010210276
## [856,] 0.514410956 0.781990125
## [857,] 0.210855994 1.666311915
## [858,] 0.433536336 0.051668382
## [859,] 0.471793455 1.904869927
## [860,] 0.025406953 0.758656880
## [861,] 0.705570185 1.228151673
## [862,] 0.976182405 0.199185092
## [863,] 0.565607104 1.503482550
## [864,] 0.244929639 0.706622847
## [865,] 0.796780595 0.292178210
## [866,] 1.668887856 0.385011344
## [867,] 1.358353503 0.934025096
## [868,] 1.414826422 0.962971027
## [869,] 0.992212431 1.796442649
## [870,] -0.004657223 1.037687509
## [871,] 2.289349166 3.058566605
## [872,] 0.912435480 1.133337660
## [873,] 1.507626830 0.495535192
## [874,] 0.975597014 0.792997118
## [875,] 0.343801882 0.441289135
## [876,] 1.470825928 0.853401892
## [877,] 1.503357511 0.894713672
## [878,] 2.858147530 1.577977647
## [879,] 1.353833399 0.740129282
## [880,] 0.889094076 0.763670768
## [881,] 0.511204724 0.540320537
## [882,] 0.488919059 0.690013593
## [883,] 0.856875585 1.053890885
## [884,] 0.730107910 1.322987313
## [885,] 1.018391363 0.279135618
## [886,] 0.491145788 0.665242505
## [887,] 0.587556978 0.523977202
## [888,] 2.443823241 1.198118629
## [889,] 0.263499743 0.403983388
## [890,] 0.334388301 0.755049895
## [891,] 0.122986363 0.143743189
## [892,] 1.216378439 -0.015478455
## [893,] 1.114307843 0.594655057
## [894,] 1.453474531 0.302389665
## [895,] 2.114180158 0.544790454
## [896,] 3.472833242 0.979227776
## [897,] 1.374476918 1.678486825
## [898,] 1.145568205 0.663628670
## [899,] 0.792973257 0.448674450
## [900,] 1.842539814 -0.091609036
## [901,] 0.603608815 0.463213246
## [902,] 1.260772097 0.400065256
## [903,] 0.280864392 1.077163521
## [904,] 2.977643486 1.292691548
## [905,] 1.507477600 0.376019574
## [906,] 0.073648508 0.597157413
## [907,] 0.805534010 2.198076595
## [908,] 0.822293379 0.819089958
## [909,] 0.401275495 1.071688638
## [910,] 0.292604294 0.584921952
## [911,] 2.183461384 0.356128775
## [912,] 1.132094998 0.768459174
## [913,] 0.737442898 0.669880956
## [914,] 1.359343716 1.311450751
## [915,] 1.433077113 1.286534926
## [916,] 0.243984125 1.074631574
## [917,] 1.573701717 0.172892590
## [918,] 1.040469231 0.680792608
## [919,] 2.175618189 2.092032102
## [920,] 1.883664184 0.526165792
## [921,] 0.759234526 1.458585932
## [922,] 1.073250436 0.506595501
## [923,] 0.915492179 0.262445623
## [924,] 0.267044391 1.238772896
## [925,] 0.993139577 0.317424967
## [926,] 0.285973100 1.634152317
## [927,] 0.220217805 0.133700778
## [928,] 1.130601463 0.528247381
## [929,] 0.819980815 1.069619177
## [930,] 0.456838107 1.142211965
## [931,] 1.279308882 1.180452322
## [932,] 0.202720128 0.875402896
## [933,] 1.704693912 0.273138620
## [934,] 1.158667038 0.273345132
## [935,] 1.841131915 0.527824894
## [936,] 1.034772261 1.097550868
## [937,] 1.358218437 0.287118907
## [938,] 0.856674797 1.460173151
## [939,] 1.364009709 2.480036397
## [940,] 0.798838238 0.391978203
## [941,] 1.929733051 1.016986335
## [942,] 1.562844776 1.129780161
## [943,] 1.262083999 1.579317632
## [944,] 1.254683338 0.357480781
## [945,] 1.164413762 2.310925434
## [946,] 1.865713738 -0.088764945
## [947,] 0.319366720 1.461612914
## [948,] 0.997362645 1.301539424
## [949,] 0.422148977 0.525506589
## [950,] 1.469132846 1.131928732
## [951,] 0.760304677 1.112038868
## [952,] 2.433821066 0.078777643
## [953,] 1.371351368 0.405600538
## [954,] 1.106531482 1.000872786
## [955,] 0.255449380 0.177782934
## [956,] 1.005235131 1.995133219
## [957,] 0.108478357 0.379906867
## [958,] 1.708280748 1.809313388
## [959,] 1.301002101 2.187265696
## [960,] 0.917394370 0.931117871
## [961,] 0.159759546 1.316662828
## [962,] 0.229038795 0.468343557
## [963,] 0.787109517 0.139299685
## [964,] 0.104651130 0.034438573
## [965,] 1.367041211 2.491216203
## [966,] 0.308434146 0.659628497
## [967,] 0.926527451 1.250044568
## [968,] 0.651346821 1.120429335
## [969,] 0.608853370 1.135222853
## [970,] 0.840105101 1.119485529
## [971,] 1.979794863 0.868885541
## [972,] 0.529531604 0.796266732
## [973,] 1.188591087 0.503317672
## [974,] 0.215607012 0.654324785
## [975,] 0.475073077 1.901282651
## [976,] 0.508168503 0.190942196
## [977,] 1.085135505 0.349670582
## [978,] 2.029647658 1.401630255
## [979,] 0.127483163 0.208581497
## [980,] 1.878274963 1.347008594
## [981,] 0.692305763 0.736566935
## [982,] 1.470200164 1.769319129
## [983,] 0.387185437 1.893553633
## [984,] 1.031871168 3.124225172
## [985,] 2.906210045 1.121383485
## [986,] 0.346935018 0.776083271
## [987,] 1.068789728 0.499599920
## [988,] 2.441558611 1.403159136
## [989,] 1.617549955 0.684828497
## [990,] 0.571907917 0.647061419
## [991,] 1.922870880 1.226333612
## [992,] 1.964211572 0.319326597
## [993,] 1.007545563 1.640456054
## [994,] 0.822789059 1.386321371
## [995,] 1.250619171 1.697889887
## [996,] 0.070102314 1.730792977
## [997,] 0.035694890 1.259148551
## [998,] 1.455178476 0.724351613
## [999,] 0.754631167 0.816081018
##
## $model.matrix
## (Intercept) avlength avcondition T_av O2_sat_av Con_av COD_av NH4._av
## 1 1 46.26316 0.7032430 11.114 81.286 740.7140 13.333 0.158
## 2 1 38.30000 0.6196317 10.440 65.400 609.6000 1.400 0.774
## 3 1 47.20000 0.7209293 12.858 75.333 434.7500 25.917 2.251
## 4 1 33.60000 0.7279046 13.086 72.857 949.0000 25.000 5.000
## 5 1 33.30769 0.6910252 11.750 96.833 896.1670 14.000 0.303
## 6 1 35.05263 0.7161573 13.557 84.571 340.7140 29.000 0.252
## 7 1 35.53333 0.7532778 11.750 88.417 716.3330 16.583 0.393
## 9 1 41.66667 0.8590551 12.008 87.000 800.3330 21.917 0.468
## 11 1 39.89474 0.6943120 13.550 63.167 527.6670 45.500 3.233
## 12 1 32.90909 0.6629259 14.957 65.857 1089.8570 50.500 5.365
## 13 1 39.88235 0.7324076 14.486 90.429 771.7143 11.333 0.668
## 14 1 38.42857 0.7578120 11.983 77.667 472.3330 37.167 2.367
## 15 1 34.23077 0.7642545 11.577 85.308 591.4620 24.667 0.260
## 16 1 43.61111 0.6500428 12.443 87.857 462.5710 37.167 0.210
## 17 1 40.16667 0.7486984 11.425 73.250 470.3330 20.000 2.589
## 18 1 37.43750 0.9549238 16.214 61.000 422.4290 35.833 2.417
## 19 1 41.47059 0.6295888 9.317 94.900 812.8330 7.500 0.103
## 20 1 28.00000 0.7382850 12.957 85.571 851.2860 15.833 0.170
## 21 1 37.84211 0.7477438 15.000 85.333 673.1670 19.500 0.635
## 22 1 42.00000 0.6865490 10.475 73.833 255.8330 24.727 0.527
## 23 1 36.50000 0.7425513 10.050 80.000 138.6670 37.167 0.290
## 24 1 43.58333 0.8243429 11.773 66.091 593.0910 34.727 0.806
## 26 1 31.72222 0.8809334 12.167 80.333 897.4170 16.417 0.409
## 28 1 40.31579 0.7644699 13.433 99.667 801.0000 20.909 0.354
## 29 1 39.25000 0.8383703 14.133 87.667 669.6670 14.000 0.227
## 30 1 42.37500 0.7907723 13.542 48.833 542.4170 42.333 2.261
## 31 1 42.00000 0.8148913 12.867 70.333 447.3330 29.333 0.680
## 32 1 38.87500 0.6351264 13.186 91.857 617.5710 19.333 0.362
## 33 1 37.31579 0.7512399 15.233 84.000 539.3330 13.667 0.325
## 34 1 38.73684 0.6217274 12.050 92.583 659.8330 14.750 0.336
## 35 1 37.85714 0.8194431 11.175 91.375 687.3750 26.000 0.395
## 36 1 32.88889 0.6862546 13.700 88.833 755.5000 26.167 1.260
## 38 1 34.65000 0.6616806 13.633 77.500 667.0000 20.333 0.695
## 39 1 33.25000 0.7554143 11.333 43.750 848.3330 35.750 2.542
## 40 1 36.75000 0.6487052 12.900 71.400 635.0000 16.400 2.208
## 41 1 36.35000 0.8265861 15.100 94.000 716.3330 15.167 0.165
## 42 1 35.15789 0.7600249 13.786 89.571 705.7140 18.167 0.880
## Nt_av pool_riffle1 meander1 netcen updist
## 1 8.917000 -1 -1 65212.97 67745.125
## 2 4.780000 1 1 50877.11 52437.119
## 3 8.925000 1 -1 38651.53 32574.449
## 4 9.067000 -1 -1 63911.70 65226.644
## 5 5.167000 1 -1 64168.17 67952.655
## 6 1.617000 1 1 45262.05 45780.074
## 7 2.775000 1 1 72386.11 76509.324
## 9 6.083000 1 -1 47724.46 49932.683
## 11 5.750000 1 1 49875.30 52217.733
## 12 16.100000 -1 -1 61880.37 26695.488
## 13 6.533000 1 1 60618.70 25511.682
## 14 7.000000 1 1 56056.62 15064.968
## 15 2.608000 -1 1 63687.75 67470.687
## 16 1.730000 -1 -1 68548.11 72561.660
## 17 10.617000 -1 -1 45271.82 39387.485
## 18 5.450000 1 -1 44142.92 15837.759
## 19 5.358361 -1 -1 64632.42 67396.486
## 20 4.583000 1 -1 72865.43 76898.411
## 21 5.067000 -1 1 58440.64 21751.460
## 22 2.164000 1 1 47879.02 44196.470
## 23 1.372000 1 1 53511.26 49989.625
## 24 4.891000 1 -1 37413.39 35027.425
## 26 5.242000 1 1 59347.24 62693.461
## 28 4.636000 1 -1 45740.16 46890.918
## 29 7.550000 -1 -1 73590.70 39137.994
## 30 3.317000 1 1 45131.08 29684.138
## 31 3.283000 1 1 43713.21 2368.891
## 32 4.767000 -1 -1 55885.32 18797.654
## 33 3.683000 1 1 63398.00 26850.462
## 34 8.050000 -1 1 65158.98 30465.362
## 35 4.317000 -1 -1 59901.23 62281.614
## 36 5.567000 -1 1 63856.37 66416.408
## 38 7.033000 1 1 53189.12 16286.394
## 39 5.017000 1 1 63663.04 23736.389
## 40 3.243000 1 1 60384.21 20784.664
## 41 2.550000 -1 -1 60481.19 20943.659
## 42 3.450000 1 1 64836.74 25423.656
##
## $terms
## spe.hel_bray ~ avlength + avcondition + T_av + O2_sat_av + Con_av +
## COD_av + NH4._av + Nt_av + pool_riffle + meander + netcen +
## updist
## attr(,"variables")
## list(spe.hel_bray, avlength, avcondition, T_av, O2_sat_av, Con_av,
## COD_av, NH4._av, Nt_av, pool_riffle, meander, netcen, updist)
## attr(,"factors")
## avlength avcondition T_av O2_sat_av Con_av COD_av NH4._av Nt_av
## spe.hel_bray 0 0 0 0 0 0 0 0
## avlength 1 0 0 0 0 0 0 0
## avcondition 0 1 0 0 0 0 0 0
## T_av 0 0 1 0 0 0 0 0
## O2_sat_av 0 0 0 1 0 0 0 0
## Con_av 0 0 0 0 1 0 0 0
## COD_av 0 0 0 0 0 1 0 0
## NH4._av 0 0 0 0 0 0 1 0
## Nt_av 0 0 0 0 0 0 0 1
## pool_riffle 0 0 0 0 0 0 0 0
## meander 0 0 0 0 0 0 0 0
## netcen 0 0 0 0 0 0 0 0
## updist 0 0 0 0 0 0 0 0
## pool_riffle meander netcen updist
## spe.hel_bray 0 0 0 0
## avlength 0 0 0 0
## avcondition 0 0 0 0
## T_av 0 0 0 0
## O2_sat_av 0 0 0 0
## Con_av 0 0 0 0
## COD_av 0 0 0 0
## NH4._av 0 0 0 0
## Nt_av 0 0 0 0
## pool_riffle 1 0 0 0
## meander 0 1 0 0
## netcen 0 0 1 0
## updist 0 0 0 1
## attr(,"term.labels")
## [1] "avlength" "avcondition" "T_av" "O2_sat_av" "Con_av"
## [6] "COD_av" "NH4._av" "Nt_av" "pool_riffle" "meander"
## [11] "netcen" "updist"
## attr(,"order")
## [1] 1 1 1 1 1 1 1 1 1 1 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"# environmental variables
env_select <- environment2[,c("T_av", "O2_sat_av", "Con_av", "COD_av", "NH4._av", "Nt_av", "pool_riffle", "meander", "netcen", "updist")]
env_select$pool_riffle <- as.numeric(env_select$pool_riffle)
env_select$meander <- as.numeric(env_select$meander)
pca <- prcomp(env_select, scale.=T)
summary(pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.7124 1.5545 1.1221 1.0140 0.88807 0.79463 0.56647
## Proportion of Variance 0.2933 0.2416 0.1259 0.1028 0.07887 0.06314 0.03209
## Cumulative Proportion 0.2933 0.5349 0.6608 0.7636 0.84248 0.90563 0.93771
## PC8 PC9 PC10
## Standard deviation 0.50483 0.46939 0.38429
## Proportion of Variance 0.02549 0.02203 0.01477
## Cumulative Proportion 0.96320 0.98523 1.00000plot(pca)
biplot(pca)
# Assess the effect of environmental variables on parasite component community dissimilarities using distance based RDA
spe.rda <- dbrda(spe.hel_bray ~ T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander, data = env_select)
anova(spe.rda)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = spe.hel_bray ~ T_av + O2_sat_av + Con_av + COD_av + NH4._av + Nt_av + pool_riffle + meander, data = env_select)
## Df SumOfSqs F Pr(>F)
## Model 8 1.1664 1.2909 0.166
## Residual 28 3.1624RsquareAdj(spe.rda)$adj.r.squared## [1] 0.06072755mod0 <- dbrda(spe.hel_bray ~ 1, env_select[,-c(9:10)]) # Model with intercept only #edit_PH
mod1 <- dbrda(spe.hel_bray ~ ., env_select[,-c(9:10)]) # Model with all explanatory variables #edit_PH
step.res <- ordiR2step(mod0, mod1, direction = "both",perm.max = 200)## Step: R2.adj= 0
## Call: spe.hel_bray ~ 1
##
## R2.adjusted
## <All variables> 0.060727547
## + T_av 0.036398792
## + NH4._av 0.020208612
## + meander 0.018502880
## + O2_sat_av 0.004277611
## + Con_av 0.001872668
## + pool_riffle 0.001742860
## <none> 0.000000000
## + Nt_av -0.002060170
## + COD_av -0.017968936
##
## Df AIC F Pr(>F)
## + T_av 1 54.788 2.3599 0.07 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1step.res$anova # Summary table## NULLplot(spe.rda, scaling = 1) # it is for technical reasons not possible to plot both site and species scores
summary(spe.rda)##
## Call:
## dbrda(formula = spe.hel_bray ~ T_av + O2_sat_av + Con_av + COD_av + NH4._av + Nt_av + pool_riffle + meander, data = env_select)
##
## Partitioning of squared Bray distance:
## Inertia Proportion
## Total 4.329 1.0000
## Constrained 1.166 0.2695
## Unconstrained 3.162 0.7305
##
## Eigenvalues, and their contribution to the squared Bray distance
##
## Importance of components:
## dbRDA1 dbRDA2 dbRDA3 dbRDA4 dbRDA5 dbRDA6
## Eigenvalue 0.5740 0.3372 0.17566 0.09415 0.042525 0.0020670
## Proportion Explained 0.1326 0.0779 0.04058 0.02175 0.009824 0.0004775
## Cumulative Proportion NA NA NA NA NA NA
## idbRDA1 idbRDA2 MDS1 MDS2 MDS3 MDS4 MDS5
## Eigenvalue -0.014797 -0.04441 1.3249 0.8027 0.4680 0.31323 0.29121
## Proportion Explained 0.003418 0.01026 0.3061 0.1854 0.1081 0.07236 0.06727
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS6 MDS7 MDS8 MDS9 MDS10 MDS11 MDS12
## Eigenvalue 0.13644 0.11438 0.08987 0.07065 0.06425 0.02766 0.01576
## Proportion Explained 0.03152 0.02642 0.02076 0.01632 0.01484 0.00639 0.00364
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS13 MDS14 iMDS1 iMDS2 iMDS3
## Eigenvalue 0.011872 2.725e-04 -0.0020314 -0.007356 -0.01078
## Proportion Explained 0.002743 6.295e-05 0.0004693 0.001699 0.00249
## Cumulative Proportion NA NA NA NA NA
## iMDS4 iMDS5 iMDS6 iMDS7 iMDS8 iMDS9
## Eigenvalue -0.014744 -0.01935 -0.022495 -0.029849 -0.03342 -0.041489
## Proportion Explained 0.003406 0.00447 0.005197 0.006895 0.00772 0.009584
## Cumulative Proportion NA NA NA NA NA NA
## iMDS10 iMDS11 iMDS12 iMDS13 iMDS14
## Eigenvalue -0.05984 -0.06439 -0.06995 -0.09116 -0.10203
## Proportion Explained 0.01382 0.01487 0.01616 0.02106 0.02357
## Cumulative Proportion NA NA NA NA NA
##
## Accumulated constrained eigenvalues
## Importance of components:
## dbRDA1 dbRDA2 dbRDA3 dbRDA4 dbRDA5 dbRDA6 idbRDA1
## Eigenvalue 0.5740 0.3372 0.1757 0.09415 0.04252 0.002067 -0.01480
## Proportion Explained 0.4921 0.2891 0.1506 0.08072 0.03646 0.001772 0.01269
## Cumulative Proportion NA NA NA NA NA NA NA
## idbRDA2
## Eigenvalue -0.04441
## Proportion Explained 0.03807
## Cumulative Proportion NA
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 3.533199
##
##
## Site scores (weighted sums of species scores)
##
## dbRDA1 dbRDA2 dbRDA3 dbRDA4 dbRDA5 dbRDA6
## 1 -0.07530 -0.62580 -0.63602 -2.643196 -0.34427 -23.8946
## 2 0.87242 0.77832 -0.32068 2.074749 -0.25800 41.3754
## 3 -0.81094 1.29200 -0.57735 -0.708420 -2.35800 -5.0035
## 4 -0.97450 0.91051 1.79849 -2.192492 -1.99248 -4.7134
## 5 -0.25889 0.74559 -1.30995 -1.418227 0.33185 -21.9720
## 6 0.41640 1.01888 -0.34551 -2.318324 1.61924 -24.8949
## 7 -0.98286 -0.67495 0.62017 1.174218 1.55155 -0.8974
## 8 0.93807 0.62635 -3.10878 -0.765073 2.41981 -3.0764
## 9 -0.62816 -0.77245 -1.30540 1.806170 1.30543 -41.2679
## 10 -1.03727 -0.50089 0.62572 0.369727 1.00378 4.7819
## 11 1.10743 -0.99448 -0.66515 -0.071830 0.83402 -14.8528
## 12 0.61624 0.76828 0.87110 0.357836 0.12125 38.2552
## 13 -0.96088 -0.72453 0.47977 1.569869 1.43399 -6.8692
## 14 1.41128 2.03453 -1.67256 0.776255 0.27648 29.9435
## 15 -1.00448 -0.59302 0.65010 0.833950 1.44643 3.4902
## 16 1.26382 0.81623 3.19230 1.380128 1.12523 -10.4959
## 17 -0.92726 -0.73637 0.33774 1.892276 1.07787 -8.2400
## 18 -1.04523 -1.01280 -0.59052 2.897871 0.35870 -14.2865
## 19 0.89987 -1.54255 -0.54435 1.738188 -2.61939 32.8867
## 20 -0.14608 -0.34079 0.78630 1.316880 1.70249 -9.1246
## 21 -1.30124 -1.46318 -1.27370 3.739798 0.91928 -16.7232
## 22 -0.07485 1.73636 0.30728 -1.760677 -0.46041 6.1504
## 23 -0.18008 -0.64105 0.55428 -1.310793 -0.82437 -3.6579
## 24 0.53054 0.08088 1.31623 -3.529128 -1.96164 41.6937
## 25 -0.50901 -0.82222 -1.26480 2.167458 -6.71178 -22.4535
## 26 0.54874 0.92050 1.12409 0.009258 0.07714 34.0773
## 27 1.25804 -0.69768 2.37255 -1.697654 -3.24936 -16.2349
## 28 -0.86554 0.32492 -0.68347 0.704689 -0.16329 13.0277
## 29 1.82276 0.98120 0.33891 -1.852448 4.33581 -8.6587
## 30 0.72974 -0.59907 -1.00943 -0.201449 1.77124 -4.5186
## 31 -1.02904 0.19515 1.05102 -1.468951 -1.36610 23.1359
## 32 -0.13877 -0.06050 -0.19569 -1.899083 -1.10403 -31.4856
## 33 1.01209 -0.59332 2.58400 -0.318337 -1.43566 39.0635
## 34 -0.39319 -1.79743 0.20497 -0.362972 -0.59693 -19.8004
## 35 -0.89506 1.46497 -0.05622 -1.876722 -1.86120 3.5844
## 36 0.87386 1.17335 -3.50567 1.196922 1.51036 -9.4970
## 37 -0.06267 -0.67495 -0.14975 0.389535 2.08495 11.1529
##
##
## Site constraints (linear combinations of constraining variables)
##
## dbRDA1 dbRDA2 dbRDA3 dbRDA4 dbRDA5 dbRDA6
## 1 -0.16605 -0.69736 -0.45052 0.39949 -1.140870 0.26766
## 2 -0.07683 -0.33692 0.61638 0.50606 -0.381728 1.66676
## 3 -0.10071 0.76299 0.51759 0.02910 0.003366 -0.26259
## 4 -1.55548 0.90013 0.66933 -0.51241 -0.415318 -0.13093
## 5 -0.41914 0.27994 -0.66104 -1.10909 0.384267 0.50060
## 6 0.63090 0.30135 -0.07724 0.37780 0.857007 -0.33310
## 7 -0.15252 -0.25647 -0.20458 -0.38652 0.792931 0.48033
## 8 -0.14874 0.12938 -0.39866 -0.78891 0.083814 0.27677
## 9 -0.14359 0.11628 0.95439 -0.04629 0.609992 -0.64671
## 10 -0.74154 -0.65088 0.87072 -1.17151 -0.875610 -1.73263
## 11 0.77571 -0.48314 0.02321 -0.76003 0.259283 -0.01494
## 12 -0.44387 -0.28442 0.54938 -0.01813 1.062736 -0.71676
## 13 -0.25608 -0.61494 -0.47847 0.74269 0.180828 -0.14346
## 14 -0.23636 0.67472 -0.87185 0.72759 0.046993 -0.61306
## 15 -0.75508 0.19781 0.41035 0.89449 -0.744271 -0.25040
## 16 0.97946 1.50039 0.74911 0.12808 -0.657115 -0.30363
## 17 -1.19679 -0.18091 -0.90174 0.16093 -0.229189 0.70454
## 18 0.23197 0.39183 -0.42932 -0.82808 -0.242760 0.64991
## 19 0.80868 -0.55920 -0.22435 0.32426 -0.445610 -0.51746
## 20 -0.19206 0.12003 0.24728 0.99541 0.747110 0.40576
## 21 -0.38477 0.09371 -0.03632 1.07969 1.383306 -0.25819
## 22 0.02352 0.34014 0.07462 0.03662 -0.352806 0.35626
## 23 0.18716 -0.91860 -0.02126 -0.69548 0.117329 0.60753
## 24 0.03851 0.57991 -0.70662 -1.10691 0.533700 -0.10095
## 25 0.61401 -0.01017 -0.51875 0.25795 -1.076732 -0.24901
## 26 0.42275 0.07490 0.99168 0.37950 -0.249480 0.19511
## 27 0.54478 -0.12356 0.31406 0.41569 0.237094 0.09772
## 28 0.02044 0.43106 -0.71781 0.33274 -0.428303 -0.26278
## 29 1.23235 0.15293 0.10463 -0.07285 0.107338 0.07885
## 30 0.02214 -1.33353 -0.37271 0.38762 -0.062960 -0.48751
## 31 -0.71637 0.11204 -0.85950 0.24734 -0.066602 -0.17455
## 32 0.01756 -0.71899 -0.27770 0.01727 0.106473 -0.73172
## 33 0.78296 -0.75806 0.27650 -0.27767 -0.031297 -0.01895
## 34 -0.33590 -0.63554 1.07989 -0.07860 -0.526645 0.97933
## 35 -0.15761 0.49842 0.66426 -0.05091 0.331527 0.71522
## 36 0.53360 0.88375 -0.84613 0.03571 -0.630019 -0.08456
## 37 0.31300 0.02099 -0.05877 -0.57261 0.712218 0.05155
##
##
## Biplot scores for constraining variables
##
## dbRDA1 dbRDA2 dbRDA3 dbRDA4 dbRDA5 dbRDA6
## T_av 0.63982 0.25527 0.1662 -0.34674 -0.2289 -0.48193
## O2_sat_av 0.02371 -0.02938 -0.8854 -0.18557 0.2035 -0.13356
## Con_av -0.25315 -0.27391 -0.1407 -0.76998 -0.4264 0.07657
## COD_av -0.13235 0.15034 0.4807 0.08407 0.1430 -0.57341
## NH4._av -0.40051 0.14159 0.7569 -0.21438 -0.2051 -0.37005
## Nt_av -0.31714 -0.23687 0.2952 -0.33830 -0.5347 -0.40917
## pool_riffle -0.34609 -0.15029 -0.4384 0.27287 -0.5547 -0.42271
## meander -0.33563 0.52703 -0.3800 -0.21165 -0.5422 -0.13159anova(spe.rda)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = spe.hel_bray ~ T_av + O2_sat_av + Con_av + COD_av + NH4._av + Nt_av + pool_riffle + meander, data = env_select)
## Df SumOfSqs F Pr(>F)
## Model 8 1.1664 1.2909 0.148
## Residual 28 3.1624anova(spe.rda, by="term")## Permutation test for dbrda under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = spe.hel_bray ~ T_av + O2_sat_av + Con_av + COD_av + NH4._av + Nt_av + pool_riffle + meander, data = env_select)
## Df SumOfSqs F Pr(>F)
## T_av 1 0.2734 2.4210 0.049 *
## O2_sat_av 1 0.1377 1.2195 0.309
## Con_av 1 0.1613 1.4283 0.213
## COD_av 1 0.0676 0.5990 0.663
## NH4._av 1 0.1709 1.5131 0.202
## Nt_av 1 0.0501 0.4439 0.784
## pool_riffle 1 0.0657 0.5818 0.658
## meander 1 0.2396 2.1210 0.084 .
## Residual 28 3.1624
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova.cca(spe.rda, step=1000);## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = spe.hel_bray ~ T_av + O2_sat_av + Con_av + COD_av + NH4._av + Nt_av + pool_riffle + meander, data = env_select)
## Df SumOfSqs F Pr(>F)
## Model 8 1.1664 1.2909 0.172
## Residual 28 3.1624anova.cca(spe.rda, step=1000, by="term");## Permutation test for dbrda under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = spe.hel_bray ~ T_av + O2_sat_av + Con_av + COD_av + NH4._av + Nt_av + pool_riffle + meander, data = env_select)
## Df SumOfSqs F Pr(>F)
## T_av 1 0.2734 2.4210 0.047 *
## O2_sat_av 1 0.1377 1.2195 0.317
## Con_av 1 0.1613 1.4283 0.227
## COD_av 1 0.0676 0.5990 0.666
## NH4._av 1 0.1709 1.5131 0.217
## Nt_av 1 0.0501 0.4439 0.790
## pool_riffle 1 0.0657 0.5818 0.695
## meander 1 0.2396 2.1210 0.065 .
## Residual 28 3.1624
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1RsquareAdj(spe.rda)$adj.r.squared;## [1] 0.06072755RsquareAdj(spe.rda)$r.squared## [1] 0.26945488.1.2 Effect of space on component community structure
# Same for spatial predictors
spe.rda <- dbrda(spe.hel_bray ~ netcen + updist, data = env_select)
anova(spe.rda)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = spe.hel_bray ~ netcen + updist, data = env_select)
## Df SumOfSqs F Pr(>F)
## Model 2 0.5154 2.2975 0.024 *
## Residual 34 3.8135
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1RsquareAdj(spe.rda)$adj.r.squared## [1] 0.06723433mod0 <- dbrda(spe.hel_bray ~ 1, env_select[,c(9:10)]) # Model with intercept only #edit_PH
mod1 <- dbrda(spe.hel_bray ~ ., env_select[,c(9:10)]) # Model with all explanatory variables #edit_PH
step.res <- ordiR2step(mod0, mod1, direction = "both",perm.max = 200)## Step: R2.adj= 0
## Call: spe.hel_bray ~ 1
##
## R2.adjusted
## <All variables> 0.06723433
## + updist 0.04867127
## + netcen 0.04317133
## <none> 0.00000000
##
## Df AIC F Pr(>F)
## + updist 1 54.314 2.8418 0.03 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Step: R2.adj= 0.04867127
## Call: spe.hel_bray ~ updist
##
## R2.adjusted
## + netcen 0.06723433
## <All variables> 0.06723433
## <none> 0.04867127step.res$anova # Summary table## R2.adj Df AIC F Pr(>F)
## + updist 0.048671 1 54.314 2.8418 0.03 *
## <All variables> 0.067234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1RsquareAdj(spe.rda)$adj.r.squared## [1] 0.06723433anova.cca(spe.rda, step=1000);## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = spe.hel_bray ~ netcen + updist, data = env_select)
## Df SumOfSqs F Pr(>F)
## Model 2 0.5154 2.2975 0.021 *
## Residual 34 3.8135
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova.cca(spe.rda, step=1000, by="term");## Permutation test for dbrda under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = spe.hel_bray ~ netcen + updist, data = env_select)
## Df SumOfSqs F Pr(>F)
## netcen 1 0.3019 2.6920 0.043 *
## updist 1 0.2134 1.9029 0.120
## Residual 34 3.8135
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1RsquareAdj(spe.rda)$adj.r.squared;## [1] 0.06723433RsquareAdj(spe.rda)$r.squared## [1] 0.11905468.1.3 Variation partitioning
#Variation partitioning
spe.varpart1 <- varpart(spe.hel_bray, env_select[,1:8], env_select[,9:10])
plot(spe.varpart1,digits=2)
spe.varpart1##
## Partition of squared Bray distance in dbRDA
##
## Call: varpart(Y = spe.hel_bray, X = env_select[, 1:8], env_select[,
## 9:10])
##
## Explanatory tables:
## X1: env_select[, 1:8]
## X2: env_select[, 9:10]
##
## No. of explanatory tables: 2
## Total variation (SS): 4.3288
## No. of observations: 37
##
## Partition table:
## Df R.squared Adj.R.squared Testable
## [a+c] = X1 8 0.26945 0.06073 TRUE
## [b+c] = X2 2 0.11905 0.06723 TRUE
## [a+b+c] = X1+X2 10 0.36716 0.12376 TRUE
## Individual fractions
## [a] = X1|X2 8 0.05653 TRUE
## [b] = X2|X1 2 0.06303 TRUE
## [c] 0 0.00420 FALSE
## [d] = Residuals 0.87624 FALSE
## ---
## Use function 'dbrda' to test significance of fractions of interestanova.cca(dbrda(spe.hel_bray ~ T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + Condition(netcen + updist),
data=env_select), step=1000)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = spe.hel_bray ~ T_av + O2_sat_av + Con_av + COD_av + NH4._av + Nt_av + pool_riffle + meander + Condition(netcen + updist), data = env_select)
## Df SumOfSqs F Pr(>F)
## Model 8 1.0740 1.2742 0.186
## Residual 26 2.7395anova.cca(dbrda(spe.hel_bray ~ netcen + updist+
Condition(T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander), data=env_select), step=1000)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = spe.hel_bray ~ netcen + updist + Condition(T_av + O2_sat_av + Con_av + COD_av + NH4._av + Nt_av + pool_riffle + meander), data = env_select)
## Df SumOfSqs F Pr(>F)
## Model 2 0.42295 2.0071 0.06 .
## Residual 26 2.73945
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 18.2 Infra-communities
# Infracommunities: Bray-Curtis dissimilarities are calculated at the individual host level Hellinger-transformed parasite data and then averaged within site
# A dummy parasite species is added to avoid problems with non-infected fishes
data_infra <- na.omit(data[,c(1,22:24,26:32)])
data_infra_disp <- dispweight(data_infra[,-1])
braycurtis <- vegdist(decostand(cbind(data_infra_disp,rep(1,nrow(data_infra))), na.rm=T, method="hellinger"), method="bray", na.rm=T)
meandist_bray <- meandist(braycurtis, data_infra[,1])
# Check whether Euclidean and Bray-Curtis distances are comparable
braycurtis <- vegdist(decostand(cbind(data_infra_disp,rep(1,nrow(data_infra))), na.rm=T, method="hellinger"), method="bray", na.rm=T)
meandist_bray <- meandist(braycurtis, data_infra[,1])
euc <- vegdist(decostand(cbind(data_infra_disp,rep(1,nrow(data_infra))), na.rm=T, method="hellinger"), method="euc", na.rm=T)
meandist_euc <- meandist(euc, data_infra[,1])
plot(meandist_bray[1:37,1:37], meandist_euc[1:37,1:37])
mantel(meandist_bray[1:37,1:37], meandist_euc[1:37,1:37])##
## Mantel statistic based on Pearson's product-moment correlation
##
## Call:
## mantel(xdis = meandist_bray[1:37, 1:37], ydis = meandist_euc[1:37, 1:37])
##
## Mantel statistic r: 0.9906
## Significance: 0.001
##
## Upper quantiles of permutations (null model):
## 90% 95% 97.5% 99%
## 0.179 0.232 0.294 0.346
## Permutation: free
## Number of permutations: 999adonis(meandist_bray ~ avlength + avcondition + T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + netcen +
updist, data=environment2)## 'adonis' will be deprecated: use 'adonis2' instead## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## avlength 1 0.05123 0.051226 6.1160 0.11570 0.006 **
## avcondition 1 0.00393 0.003927 0.4688 0.00887 0.627
## T_av 1 0.00874 0.008741 1.0437 0.01974 0.390
## O2_sat_av 1 0.02116 0.021158 2.5262 0.04779 0.105
## Con_av 1 0.05861 0.058606 6.9971 0.13237 0.009 **
## COD_av 1 0.03543 0.035428 4.2298 0.08002 0.032 *
## NH4._av 1 0.00275 0.002754 0.3288 0.00622 0.680
## Nt_av 1 0.01049 0.010488 1.2521 0.02369 0.293
## pool_riffle 1 0.00491 0.004914 0.5867 0.01110 0.568
## meander 1 0.00959 0.009588 1.1447 0.02166 0.324
## netcen 1 0.02319 0.023187 2.7684 0.05237 0.079 .
## updist 1 0.01171 0.011707 1.3977 0.02644 0.284
## Residuals 24 0.20102 0.008376 0.45403
## Total 36 0.44274 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $call
## adonis(formula = meandist_bray ~ avlength + avcondition + T_av +
## O2_sat_av + Con_av + COD_av + NH4._av + Nt_av + pool_riffle +
## meander + netcen + updist, data = environment2)
##
## $coefficients
## NULL
##
## $coef.sites
## [,1] [,2] [,3] [,4]
## (Intercept) 5.438577e-01 -7.521287e-02 4.864790e-01 2.572596e-01
## avlength -6.884388e-03 -1.280466e-03 -5.827549e-03 -1.940029e-03
## avcondition -9.453108e-02 4.976498e-02 -1.367366e-01 -1.391442e-01
## T_av 2.507095e-03 3.945903e-03 2.216001e-03 1.001131e-02
## O2_sat_av 1.097888e-03 1.797618e-03 -5.251344e-04 -9.855585e-04
## Con_av 1.252067e-04 1.767804e-04 2.866100e-04 6.899238e-05
## COD_av 2.766346e-03 5.270287e-03 4.180377e-03 3.645643e-03
## NH4._av -3.209605e-03 -1.389345e-02 -2.435426e-02 -4.858725e-02
## Nt_av -2.834506e-03 2.998526e-03 2.117586e-03 1.084555e-02
## pool_riffle1 -1.054452e-02 -1.173617e-02 -2.662374e-02 -5.705720e-03
## meander1 5.668373e-03 8.189203e-03 1.673440e-02 5.970137e-03
## netcen -2.142602e-06 -2.167154e-07 -2.066516e-06 2.110136e-07
## updist -1.536592e-06 -1.364493e-06 -1.425078e-06 -1.036735e-06
## [,5] [,6] [,7] [,8]
## (Intercept) 1.859992e-01 9.431975e-02 5.239507e-01 7.121103e-01
## avlength -9.061148e-04 9.115535e-04 -3.388699e-03 -4.874516e-03
## avcondition -2.246177e-02 1.242866e-02 -1.607023e-01 -2.930291e-01
## T_av 9.855242e-03 -7.934316e-04 8.314717e-03 1.226973e-02
## O2_sat_av -8.737335e-04 -5.764731e-04 -6.922634e-04 -1.430875e-03
## Con_av 7.929827e-05 1.627319e-04 1.178433e-04 -8.169731e-06
## COD_av 2.620073e-03 2.450642e-03 2.425527e-03 1.291031e-03
## NH4._av -2.383806e-02 -1.545591e-02 -2.488679e-02 -1.121646e-02
## Nt_av 6.508375e-03 4.792203e-03 3.250168e-03 1.733526e-03
## pool_riffle1 -2.181549e-02 -1.025280e-02 -2.009409e-02 -2.039528e-02
## meander1 1.696135e-02 5.007047e-03 -1.235392e-04 5.064015e-03
## netcen -6.091881e-07 1.199733e-06 -2.636603e-06 -2.423047e-07
## updist -8.547704e-07 -1.413130e-06 -1.292494e-06 -6.716768e-07
## [,9] [,10] [,11] [,12]
## (Intercept) 4.024582e-01 1.430932e-01 7.114198e-01 5.253185e-01
## avlength -1.263684e-03 6.480256e-03 -6.097911e-03 -1.568750e-03
## avcondition 6.327768e-02 2.153909e-01 -1.325121e-01 -2.083399e-01
## T_av 2.158539e-03 7.436242e-03 6.761999e-04 2.230340e-02
## O2_sat_av -1.641426e-04 -9.153827e-05 -1.008227e-03 -2.435431e-03
## Con_av -8.718723e-06 -2.085945e-04 1.217167e-04 1.006248e-04
## COD_av -3.012411e-04 -3.782117e-03 2.998923e-03 1.207333e-04
## NH4._av -1.764703e-02 7.858097e-04 -1.745848e-02 -2.775773e-02
## Nt_av 7.561367e-04 -9.554883e-03 -6.117636e-05 -1.001816e-03
## pool_riffle1 -6.407395e-03 1.052973e-02 -2.562772e-02 -2.466499e-02
## meander1 -9.101494e-03 -6.934907e-03 -6.056336e-03 -5.635160e-03
## netcen -6.643610e-07 7.643835e-07 -2.271691e-06 -2.617159e-06
## updist -1.278343e-06 1.330479e-07 -7.875475e-07 9.160172e-07
## [,13] [,14] [,15] [,16]
## (Intercept) 3.672792e-01 6.122057e-01 2.184547e-02 3.964502e-01
## avlength -2.868565e-03 -7.576375e-03 1.666613e-03 -4.641591e-04
## avcondition -1.957521e-01 7.839817e-02 -1.507661e-01 -2.834975e-01
## T_av 6.543640e-03 -3.778705e-03 1.269910e-02 -6.279850e-03
## O2_sat_av -1.983972e-04 1.375183e-04 -1.235877e-04 8.722842e-04
## Con_av 2.378346e-04 2.064674e-04 1.856637e-04 2.705789e-04
## COD_av 3.321870e-03 1.401488e-03 3.425972e-03 3.598149e-03
## NH4._av -1.721518e-02 -1.323472e-02 -2.514115e-02 -1.960661e-02
## Nt_av 3.107012e-03 9.907581e-03 -2.455106e-03 2.484041e-03
## pool_riffle1 -8.535036e-03 -1.634315e-02 2.783916e-03 -1.866677e-02
## meander1 5.367622e-03 2.477360e-02 8.039477e-03 2.416684e-02
## netcen -2.042112e-06 -3.535570e-06 1.554878e-07 -1.256661e-06
## updist -1.439341e-06 -1.338057e-06 -9.098917e-07 -9.217761e-07
## [,17] [,18] [,19] [,20]
## (Intercept) 2.493115e-01 8.152537e-02 3.929447e-01 3.545752e-01
## avlength -2.521561e-03 3.450389e-03 -2.006019e-03 -4.380332e-03
## avcondition -5.535822e-02 3.380327e-02 -7.982891e-02 -1.249622e-01
## T_av 1.577542e-02 1.829433e-03 -3.257684e-03 5.842857e-03
## O2_sat_av -3.670857e-04 1.115173e-03 1.586402e-04 -2.732561e-04
## Con_av 9.029447e-05 1.690298e-04 3.073729e-05 2.848146e-04
## COD_av 2.382621e-03 1.725383e-03 2.016596e-03 3.868612e-03
## NH4._av -1.804147e-02 3.178641e-03 -4.612758e-03 -2.216283e-02
## Nt_av 3.863537e-03 -2.837115e-03 2.887095e-03 3.626124e-03
## pool_riffle1 -6.032976e-03 -2.395194e-02 1.614506e-02 -2.226198e-02
## meander1 9.922600e-03 2.575902e-02 -1.634898e-02 1.245716e-02
## netcen -2.083832e-06 -2.868632e-06 3.331980e-07 -2.097165e-06
## updist -6.410051e-07 -1.222135e-06 -5.676478e-07 -1.325414e-06
## [,21] [,22] [,23] [,24]
## (Intercept) 3.775391e-01 1.503970e-01 2.250218e-01 4.573949e-01
## avlength -2.398519e-03 -2.089041e-03 1.704741e-03 -4.205541e-03
## avcondition -1.783264e-01 -7.805221e-02 -2.415759e-01 -7.956202e-02
## T_av 8.668038e-03 9.723828e-03 1.330985e-02 3.519712e-03
## O2_sat_av -5.721243e-04 1.421863e-04 -3.206484e-04 -1.767363e-03
## Con_av 3.611625e-04 -1.233660e-05 -8.074887e-05 7.022169e-05
## COD_av 3.063339e-03 1.594269e-03 1.418966e-03 2.592955e-03
## NH4._av -1.613161e-02 -1.470376e-02 -1.487983e-02 -2.498722e-02
## Nt_av 1.587171e-04 7.708938e-03 1.771918e-03 7.708403e-03
## pool_riffle1 -2.435808e-02 -8.033741e-03 3.562517e-04 -2.123952e-02
## meander1 1.578138e-02 1.091345e-02 -1.620349e-02 9.757328e-03
## netcen -3.310124e-06 1.502106e-06 1.297669e-06 7.740343e-07
## updist -1.271056e-06 -5.708792e-07 -8.476200e-07 -1.029088e-06
## [,25] [,26] [,27] [,28]
## (Intercept) 6.034100e-01 4.057286e-01 3.512245e-01 -6.049687e-02
## avlength -4.026509e-03 -5.057274e-03 -3.273197e-03 1.120816e-03
## avcondition -2.587040e-01 -4.526089e-02 -1.880925e-01 1.101614e-01
## T_av 2.666839e-03 -1.252435e-03 1.171091e-02 1.113716e-02
## O2_sat_av 4.608734e-04 1.746840e-03 -4.150943e-04 -2.836705e-04
## Con_av 2.772668e-04 -3.149822e-05 2.057587e-05 1.134627e-05
## COD_av 3.414552e-03 8.643396e-04 1.701807e-03 1.467592e-03
## NH4._av -8.437314e-03 -9.353963e-03 -1.221150e-02 -1.507347e-02
## Nt_av -3.042008e-03 8.250434e-03 6.560634e-03 7.856403e-03
## pool_riffle1 -1.612736e-02 -2.304926e-03 -4.697692e-03 2.871501e-03
## meander1 1.555900e-02 -6.834422e-03 -6.892891e-03 1.670464e-02
## netcen -4.516532e-06 6.536252e-08 4.178064e-07 -1.139741e-07
## updist -1.004042e-06 -8.459542e-07 5.086260e-07 1.580361e-07
## [,29] [,30] [,31] [,32]
## (Intercept) 5.411230e-01 2.798404e-01 2.012307e-01 1.754865e-01
## avlength -5.499696e-03 -1.806170e-03 6.207745e-04 3.526583e-03
## avcondition -1.657150e-01 -2.368554e-02 -1.803869e-01 6.531869e-02
## T_av -1.666067e-03 1.050303e-02 1.827691e-02 -4.432781e-04
## O2_sat_av -1.458978e-04 -6.885352e-04 -1.052350e-03 -4.887270e-04
## Con_av 3.310512e-04 1.118422e-04 -3.615548e-05 -1.373777e-04
## COD_av 4.519706e-03 1.850090e-03 9.873455e-04 -3.841630e-04
## NH4._av -1.908581e-02 -5.427594e-03 -2.546420e-02 -1.039064e-02
## Nt_av 2.459344e-03 -3.280792e-03 7.125438e-03 2.602391e-03
## pool_riffle1 -2.776719e-02 -2.061912e-03 7.563372e-03 2.522503e-02
## meander1 1.501418e-02 -8.648484e-03 1.197594e-03 -2.724023e-02
## netcen -3.093052e-06 -1.357062e-06 6.499642e-07 3.058410e-06
## updist -1.500177e-06 -2.615530e-07 -4.545058e-07 -1.236302e-06
## [,33] [,34] [,35] [,36]
## (Intercept) -9.926329e-02 -1.480425e-02 1.089886e-01 3.596503e-01
## avlength 4.335006e-03 1.033814e-03 -9.915825e-04 4.009826e-04
## avcondition 1.949696e-01 -4.728744e-02 4.591265e-02 -1.226052e-01
## T_av 1.536676e-03 1.917641e-02 1.128600e-02 5.420576e-04
## O2_sat_av 7.134183e-04 1.585385e-03 -7.126774e-04 -4.668302e-04
## Con_av -3.752504e-05 -6.760566e-05 -5.178517e-05 1.385763e-05
## COD_av 5.479538e-05 6.218814e-04 2.718617e-03 2.249487e-03
## NH4._av 1.808783e-02 -8.012365e-03 -4.491819e-02 -1.750323e-02
## Nt_av -5.949936e-03 5.427836e-03 1.507993e-02 1.596337e-02
## pool_riffle1 -1.132603e-02 -3.017789e-03 -5.284129e-03 1.761631e-02
## meander1 -7.888497e-03 3.928058e-03 4.956637e-03 2.447158e-02
## netcen 1.799926e-06 -1.746930e-06 5.266375e-07 -6.522442e-07
## updist 5.942911e-07 2.912431e-07 8.615626e-08 8.147668e-07
## [,37]
## (Intercept) 4.664490e-01
## avlength -1.423928e-03
## avcondition -1.186889e-01
## T_av 1.169964e-02
## O2_sat_av -1.672003e-03
## Con_av 7.064826e-06
## COD_av 1.484116e-03
## NH4._av -2.993290e-02
## Nt_av 6.515725e-03
## pool_riffle1 -1.512110e-02
## meander1 -2.765644e-03
## netcen -1.796119e-06
## updist 1.206938e-07
##
## $f.perms
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.3801145663 2.7056211854 1.483657533 -0.278562971 1.1822156008
## [2,] 0.0752748618 0.7437230511 1.354511330 0.125085560 2.5049269601
## [3,] 0.4491885507 0.4699545258 0.428958337 0.877077976 0.4184159851
## [4,] 0.8620149452 1.2718856976 2.085752120 4.355769511 0.5839857473
## [5,] 2.1758722839 0.1322823381 0.274673803 4.653055913 -0.2287795144
## [6,] -0.1654889150 -0.2060841473 2.357695685 1.403741708 0.2027964238
## [7,] 0.4548028613 -0.2607403104 1.242845113 0.039981956 2.3596960264
## [8,] 1.3635094472 0.5990752351 0.397499376 0.501293902 0.5256907685
## [9,] 0.2549926245 0.2449082104 0.137121931 0.607183394 0.8927898096
## [10,] 2.0829944456 1.2745951772 0.676069405 0.806620188 0.7947295199
## [11,] -0.0456875167 1.8986451607 0.137795040 1.690545145 0.7986318633
## [12,] -0.1074031340 0.7771250432 3.613326244 1.635354499 0.1026507103
## [13,] 0.7256707470 1.1884853968 0.394766177 0.043898642 0.4963612515
## [14,] 0.0952129435 0.5729074778 0.976376791 -0.108593034 0.9365829573
## [15,] 0.4905414554 0.1977839941 0.951472180 1.590876172 0.1605402190
## [16,] 1.3837754357 9.9144326984 4.670619364 0.386800655 0.3358365653
## [17,] 1.6951418969 0.5973844190 -0.158890160 0.105350226 0.8034096858
## [18,] 5.0285137814 0.1290078407 2.904054989 1.044073123 -0.0817006747
## [19,] 1.3538767339 0.6236096725 0.108874503 0.306143837 0.6159352388
## [20,] 1.4880519797 2.4972617218 1.330629026 0.380642748 0.0507743019
## [21,] 1.2994305115 4.8791494026 1.206475760 0.757893126 0.9654562351
## [22,] 0.0892688115 1.2400292932 0.982865993 -0.132360076 1.1821245430
## [23,] -0.1150981502 0.1131422218 4.592522004 2.925104541 -0.1053834636
## [24,] 0.1596578715 2.5246089396 0.815190665 1.209447668 1.8033392356
## [25,] 0.0995777378 1.5576240166 0.499983645 0.377200257 -0.0045452946
## [26,] 0.5377456616 0.3338557294 0.905980453 0.288422247 1.0137637937
## [27,] 0.7903829214 2.2911887855 0.670865362 11.059108710 1.7118816014
## [28,] 1.3582212386 1.4575562951 5.379600751 0.489148020 1.5480135521
## [29,] 2.1713078591 0.6301170655 1.617679092 0.198466311 0.8975342546
## [30,] 2.1773536059 0.5761896607 0.689572820 0.253966795 1.1781255208
## [31,] 0.3206328627 0.1742335826 0.501383679 0.003977202 2.3241271628
## [32,] 1.0737720619 0.7613663046 0.089049648 0.509817767 0.8075802543
## [33,] 0.6713668257 1.7409605082 -0.149413880 0.684004080 5.6574548790
## [34,] 1.3320939906 1.5155845179 0.701510545 1.578370282 0.7919368828
## [35,] 0.8523306803 0.9165769026 -0.294324258 -0.100489820 0.6132511852
## [36,] -0.0234451606 0.3281165208 -0.126269263 0.746419536 -0.0985869166
## [37,] 1.7352439663 4.2040483821 1.612428813 0.332667555 0.4279498210
## [38,] 0.4971254422 1.6483515560 2.262685812 0.340459232 0.2570874900
## [39,] 0.2009194285 0.7121760864 2.070363004 1.543297456 -0.6656348573
## [40,] 4.2660166079 -0.1439730469 1.747034260 0.046794955 1.0760637906
## [41,] 0.3992465266 1.6286970827 0.058443246 1.896999329 -0.1929586325
## [42,] 0.0945037006 4.7909242132 0.357118914 1.246863995 2.1873506946
## [43,] -0.1001649687 1.3940896322 0.456819676 0.262287617 0.1857513273
## [44,] 0.0272577142 0.1576587005 0.884839655 0.523524642 3.5652946397
## [45,] 1.6198814953 0.7318668569 0.241774460 0.227240662 2.1048032161
## [46,] 0.1914361448 0.4136031889 1.044134370 2.718716122 0.3651273869
## [47,] 1.3415712215 0.2759874701 0.110926962 0.877368276 0.6646515345
## [48,] 0.1678637395 0.2345765050 0.066532673 0.341940694 1.3754957411
## [49,] 0.6013249197 0.4220318107 0.442286120 1.384243909 1.5996776343
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## [467,] 4.6155799660 1.2822530794 5.749615457 6.476565101 5.4703690100
## [468,] -0.0484690677 2.0293119150 0.344138581 1.134242343 1.0390643959
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## [474,] 0.7182300200 2.5724390760 -0.002641194 1.419914408 3.9567245540
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## [476,] 1.7221276511 1.2056938024 0.062273434 1.594857280 0.2184811379
## [477,] 3.5951592156 -0.1008252520 2.383712165 0.356798357 1.8781466549
## [478,] 1.8906391969 0.9293173922 1.485739463 -0.332550909 0.5137652096
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## [482,] -0.1841314742 0.9934930730 0.198107291 2.012150181 0.2941851744
## [483,] 3.1408369258 0.0802547375 0.842309446 1.017023850 1.8949672328
## [484,] 0.5307644669 1.2964475384 0.724397978 0.384746970 0.3012661918
## [485,] 1.7245158380 3.3641250759 3.575493730 0.505286607 0.1413903336
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## [487,] 0.3629194556 0.6826287413 1.948991539 0.392854663 -0.1221617123
## [488,] 1.3035116553 1.0336902350 0.728722568 1.970007750 1.9303082615
## [489,] 1.2031793344 0.2598462988 0.796184214 0.036749546 1.7993549642
## [490,] 1.1682730939 1.2631044549 1.169854649 4.298950857 5.5815790674
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## [495,] 0.1969051360 2.4230636994 0.031305754 1.002871851 1.2561099696
## [496,] 1.0474819598 0.4459832165 1.219007002 0.477968700 7.6869039500
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## [498,] 0.2734930171 4.1172697017 3.298923329 0.102728143 0.4663754073
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## [503,] 0.1424666342 1.1928408243 0.506357860 0.842740765 0.9578842584
## [504,] 2.2216668132 0.4734384029 -0.065637285 0.340505084 1.2691089565
## [505,] 1.7773548308 3.0232473976 2.920474934 0.811131781 3.4123159686
## [506,] -0.2035866136 -0.0318188575 0.558429583 0.283115492 2.5524653634
## [507,] 1.6536153184 1.5422227845 1.874050079 1.211963056 -0.0099634103
## [508,] 2.7008839267 1.8105959607 0.828281191 0.383063200 0.9176699033
## [509,] 6.1254589567 3.6596794805 1.108297708 1.109406218 2.1506356957
## [510,] 1.8788571892 1.1153107614 0.818468174 0.016097255 -0.0355347309
## [511,] 0.1703517683 1.1652458163 0.629779907 -0.146189618 2.4679525658
## [512,] 2.1498880922 5.4543579808 2.557282986 3.278567598 10.2970473389
## [513,] -0.1561407847 0.9758089567 0.338591862 0.036224182 0.8595892656
## [514,] 1.0987062188 -0.0228848789 0.162009508 0.325886683 4.2657647156
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## [518,] 1.6926744351 0.0382663531 1.322279523 4.081625190 0.7569539383
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## [521,] 5.1553289479 0.7050833130 1.114577056 1.648340428 0.0419683752
## [522,] 0.6368660438 3.1328111841 1.741870764 1.601000537 1.6836523109
## [523,] 0.7976238931 1.1901723730 0.612079978 0.681546061 2.7820923389
## [524,] 1.0254138574 0.1358191074 -0.186158941 0.738890382 0.1563245904
## [525,] 1.2808532830 3.9635154745 2.384923273 1.857255758 0.2097420157
## [526,] 0.4794492459 -0.2111378597 1.098378295 1.256514912 0.5379278121
## [527,] 0.8218166847 0.9812117343 0.659508458 0.991149177 0.2977525710
## [528,] 0.3387346534 5.1684223078 1.649932660 0.012723736 1.2777582468
## [529,] 1.2962165187 1.2481707452 0.076719841 1.710539460 0.2018369963
## [530,] 0.4208640397 3.6279730822 0.562985406 3.264668661 0.6096959162
## [531,] 0.3975199742 1.3242431098 0.305597499 3.033296397 0.0390967878
## [532,] 1.2955634173 1.6003795661 1.047993340 0.773871639 0.8467352032
## [533,] 0.4872176352 3.0457989056 5.738098162 1.228492743 1.0843228141
## [534,] 0.6760763830 0.0629308411 -0.242331464 1.267662416 1.6882198628
## [535,] 1.4266503009 1.8614666949 0.132520521 3.248176720 5.3337906701
## [536,] 1.0411125631 -0.0290985012 1.468167331 0.764843846 0.4355085603
## [537,] 1.5488391070 1.7418881381 -0.074607551 0.278653835 0.2883982880
## [538,] 1.4493144162 3.4234793691 0.074794959 2.972993081 1.2138066521
## [539,] 0.1279055303 1.8567862180 0.214555245 -0.210471933 0.7601026887
## [540,] 0.1698498901 0.6124744345 0.668311011 0.667487218 0.3214443997
## [541,] 3.6479952055 0.7692955887 1.893300380 0.248699983 0.4956929296
## [542,] 0.0911694529 3.0222089185 1.454891831 0.657601122 0.2647400412
## [543,] 2.6689444675 0.2662885343 0.965085967 1.641146902 0.6385384341
## [544,] 0.9830194887 1.5363087357 0.133947038 0.449277334 0.2153151404
## [545,] 0.2440781628 0.4983840462 0.096765044 0.449245905 1.0017129515
## [546,] 1.4650796084 3.1137876295 0.900650206 2.802827455 1.0111750797
## [547,] 0.7335780271 0.2450006454 1.098871384 0.135220905 2.7788897262
## [548,] 1.3805952788 0.5697872243 -0.168377371 0.638982165 0.2124243937
## [549,] -0.0602986220 1.8688263370 2.485862155 0.909664759 3.1231774234
## [550,] 2.8083681462 0.2020594487 1.491636797 0.305763872 0.5786546859
## [551,] 1.0338511462 1.7449572238 -0.084894053 2.195467065 -0.1970814130
## [552,] 0.3025185417 3.9237499640 0.987401493 1.246029012 0.3358720302
## [553,] 1.1471320258 -0.1966531415 3.450743520 3.953933629 0.3406796608
## [554,] 4.0326953496 1.9695216128 0.581528975 0.394582388 1.9211396656
## [555,] 0.4825848303 0.2165179738 0.381628538 0.494586898 1.3009204515
## [556,] -0.1272595964 0.7503250262 1.315337463 -0.009927599 0.4851448905
## [557,] 0.6870053850 0.6160366098 -0.024898411 0.150565018 2.0469453038
## [558,] 0.4555227011 1.9067751139 2.695365314 1.352802199 0.8045993211
## [559,] 0.9322430427 0.1311962459 -0.128197223 1.658498014 0.0964037684
## [560,] 0.4320064878 0.8263684487 1.121779839 0.545813720 0.5573386523
## [561,] 4.2580595143 1.1256755309 0.180034132 2.801352040 0.2920732576
## [562,] 0.7319087105 -0.0319331515 0.121738772 0.371009105 -0.1093262188
## [563,] 0.7636390466 2.6624221966 0.141825962 3.393695866 0.7010702135
## [564,] 0.0819128375 0.9533507084 0.915054316 0.479071177 0.1854333365
## [565,] -0.0026847607 0.0476757128 1.543440517 1.142343171 0.0671608773
## [566,] 1.0127722908 1.5193545018 0.798078654 1.865217707 3.4366702264
## [567,] 0.8655589510 0.0591998628 1.702741357 6.196545831 -0.2132396215
## [568,] 0.7051261307 0.7236739548 2.603651440 3.046322178 -0.2096891006
## [569,] 1.9456308298 0.1753158149 -0.173994675 0.303922052 0.6842671976
## [570,] 1.6259650071 4.6670553749 0.115503478 0.340341878 -0.0284363072
## [571,] 4.7579708126 1.2936028387 0.126228024 1.876967204 -0.1095729447
## [572,] 1.8595085995 -0.2258795628 2.231819010 0.075550057 1.9042423905
## [573,] 0.8392922707 0.5302575208 0.916079123 0.114715975 0.4574887636
## [574,] 1.3590575906 2.7511168319 0.864931132 0.503184869 0.0101674678
## [575,] 0.2105033161 0.2730221189 0.030778370 0.163413163 0.8823181425
## [576,] 0.2655330748 0.9972455347 3.647147748 0.555315186 0.1229689315
## [577,] 1.1055902666 1.8785218275 1.620484926 0.179839293 1.0051373509
## [578,] 0.0878372797 0.4462813231 1.813296395 0.234063893 1.7816959112
## [579,] 2.0653733558 -0.0096610371 1.251901124 0.527628414 1.7867711039
## [580,] 1.4055070968 1.9127129746 -0.068347393 0.131338793 0.3711550738
## [581,] 0.6709347383 2.6173026985 0.599017356 0.752938700 2.0876013858
## [582,] 0.0981786294 0.7942268475 1.213826693 1.194125578 0.3185238521
## [583,] 0.0686515947 -0.0732240956 1.317556820 0.334221487 0.9782328616
## [584,] 0.1454192196 -0.0609604066 1.943164370 0.092961191 0.1345861491
## [585,] 1.0550099287 0.3922655124 1.947470674 0.024930848 0.6614204243
## [586,] 2.5885811586 0.8473647525 -0.051866735 1.698373646 1.2129729018
## [587,] 2.3728129531 2.5655496685 0.966335119 0.523289699 0.3611004242
## [588,] -0.0546890297 -0.0794420470 0.282451027 1.922149859 0.1984774911
## [589,] 0.0798384417 0.0801523099 0.198246222 0.171472519 0.9151672279
## [590,] 1.6272042824 1.2651036288 1.481553974 0.794129588 0.9317672966
## [591,] 1.3944303360 0.5576265259 1.403553142 0.090344906 2.6054750190
## [592,] 1.1408030684 0.4925310079 1.728961045 1.749466508 1.3662969802
## [593,] 0.4269652747 0.7960478344 1.727458539 0.930575494 0.5207948497
## [594,] 0.0089930008 0.0385181812 3.217686113 0.674031514 2.0481450577
## [595,] 1.2379614392 0.5561565231 0.170363064 0.461160114 -0.1157170209
## [596,] -0.2146026529 2.1548448476 0.248569878 1.325238194 1.7561046276
## [597,] 2.1720250361 0.5390872525 0.304849299 1.983033579 1.0729797311
## [598,] 0.0220957205 0.1965451361 0.867269718 0.422343236 -0.1047142355
## [599,] 2.2778336444 0.4642499058 2.127253787 2.207965147 0.7849578991
## [600,] 1.6372022727 3.4042742011 1.097860029 1.218758124 0.2475689159
## [601,] 0.8230581894 -0.2173229496 0.684142952 0.224573862 0.6606872295
## [602,] 0.9554369145 2.6907602636 0.425736373 1.352732015 0.4770164386
## [603,] 1.9036594658 1.3495465454 0.163952941 0.003680062 5.4209244740
## [604,] 0.0117178558 2.0451008022 2.724931725 0.506740588 2.1749516585
## [605,] 4.6115518393 0.3085430466 1.171154940 5.870876815 1.3512366038
## [606,] 0.8455204742 1.4319700804 1.439681379 -0.026633414 0.4988284720
## [607,] 6.3511027232 0.6861398854 2.213401466 2.722062569 1.5700132777
## [608,] 1.3814001544 0.1015498111 1.325196358 -0.084496861 0.2102268108
## [609,] 0.2618681264 1.0199927663 1.596214580 1.118220238 1.9498687407
## [610,] 0.1230566708 0.6110060913 0.360368694 0.157473189 1.2275926102
## [611,] 1.8910796914 0.3999741696 2.249451272 2.613901120 1.1266389091
## [612,] 1.2961178981 0.2499546089 5.871257645 0.394932304 0.7813539530
## [613,] 0.6768140094 0.1238548386 0.882992989 1.993132289 0.1823574651
## [614,] 0.7978253163 -0.0028877378 0.836178906 0.310033836 0.3804218761
## [615,] 0.0437916740 0.7040259152 1.519797700 1.157644086 0.1754988814
## [616,] 0.1191292025 0.1960670437 1.302116743 0.398254944 0.3112014233
## [617,] 0.2748314327 1.7476489879 1.417211860 0.596982053 -0.0565729693
## [618,] 0.3192492026 1.1463327321 5.625862558 2.274093526 0.9241766243
## [619,] 2.5366861223 0.0438215872 0.268589707 0.195556448 1.4331393386
## [620,] 2.1251804815 0.8389312736 1.544103491 1.481038542 0.5792194688
## [621,] -0.1096374473 0.6747195057 0.672252136 3.334578805 1.3415843655
## [622,] 1.0198175239 0.3655467529 0.305543476 0.815573716 0.8528563640
## [623,] 0.6587701696 1.0940660945 -0.072383500 2.103901776 0.3952863548
## [624,] 1.5195486981 1.6404825931 3.866806184 2.742592673 1.4094608398
## [625,] 1.9579983158 2.9089180916 0.108282358 3.214521919 0.7642414420
## [626,] 0.6471550510 0.6680259485 0.460947213 1.338542706 0.9339703700
## [627,] 0.2098823738 0.7192907609 0.178456889 2.874633847 0.5369635739
## [628,] 0.1643263649 0.5358182872 0.155879709 0.795347140 0.1215605469
## [629,] 0.6517903183 0.2105018081 0.041407983 1.993827888 0.9908287620
## [630,] 2.0919811294 1.0101343961 0.702256766 3.015227601 -0.0292866867
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## [633,] 0.2112153464 1.2797761929 1.495415338 2.286392968 1.4076241363
## [634,] 0.0592467304 0.3612814181 0.468464344 0.502167020 1.7200111137
## [635,] 1.3619255390 2.1941346358 0.338790010 2.562119301 0.0385338548
## [636,] 0.6799913132 2.1054100340 0.464986088 1.393077569 0.6313415857
## [637,] 1.5910713811 0.1176609483 3.431886736 0.678128577 0.4763520444
## [638,] 0.3059261010 1.0012771244 0.832531452 0.452294290 0.3958141420
## [639,] 1.8918286518 0.8920741868 1.871996073 0.540507590 1.2900706414
## [640,] 1.0108656380 0.0781161406 0.413303897 1.411969433 0.5962247495
## [641,] 0.4675984134 0.8092738223 1.085894295 0.414360494 3.7153723169
## [642,] 0.8838277068 1.6623129692 1.367465131 -0.141185915 0.0161382912
## [643,] 0.6993632615 -0.0034935800 0.844439673 0.966099979 1.2492988285
## [644,] 0.1167827180 2.7961923913 1.492108312 1.482290616 2.3411747259
## [645,] 0.0086065272 0.1741769987 1.055123924 2.460820874 1.0156164776
## [646,] 5.0752072120 2.1263989878 0.315963774 3.997606424 0.0318753476
## [647,] 1.2610365952 -0.3541448443 5.641341707 5.170581683 0.4183982644
## [648,] 0.8428514836 0.2859796652 0.637010778 -0.011263118 1.4229909213
## [649,] 1.9505979102 0.3439248211 0.254083462 0.595896175 0.6029879317
## [650,] 0.5004499576 1.1753784829 1.063368246 0.864098638 2.9356151170
## [651,] 2.3309771561 1.5134105083 1.467496790 1.462921892 0.0812046492
## [652,] 0.0889203596 0.9248946666 0.103764091 3.080494065 1.3430017170
## [653,] 1.0417645190 0.7794465625 0.698822704 1.508355724 0.8825458603
## [654,] -0.1362258233 0.3016891703 0.018589749 0.946286865 1.5938757688
## [655,] 0.0070953552 0.6815916552 3.484109414 0.490914156 1.1404011490
## [656,] 6.3976957406 1.2214469262 1.423338802 0.389423503 0.5809815210
## [657,] 0.0461080619 0.4953479199 1.719094110 1.322648684 0.3858357592
## [658,] 0.3946208351 -0.0282421208 1.020968121 0.186792007 0.8620356180
## [659,] 3.1786120613 1.1547825687 0.103301161 0.258826512 2.0413778466
## [660,] 0.1586540359 -0.0254306477 0.567235705 0.392566406 0.5208797846
## [661,] 0.4733918622 0.6161196725 0.299170645 0.352953036 0.2647996965
## [662,] 1.6563469734 0.2192038496 0.922800209 0.720335880 2.4231577860
## [663,] 0.9686730366 0.7941797576 0.782558051 0.292108181 0.1208233387
## [664,] 3.1984622198 0.2902749754 -0.091054605 0.321984509 0.1171550680
## [665,] 0.3599762336 1.2878381583 0.425684484 1.810887461 2.0439903831
## [666,] 1.6434829448 1.0838334666 1.066499230 1.305360340 0.0070191083
## [667,] 0.0240349483 0.8134051964 0.919026703 0.011734204 0.3553292999
## [668,] 0.0973436706 0.9582619402 -0.003921118 0.617313875 2.7173934349
## [669,] 0.4730456439 0.3748986098 2.134349617 1.573147628 0.2126045263
## [670,] -0.3832243911 3.2737065562 0.056234267 1.062600632 1.1181017263
## [671,] 1.1252382513 0.9084076961 -0.160658267 0.180814760 0.5165908001
## [672,] 0.1152104877 0.0032522412 0.204498557 0.924231962 0.8215924597
## [673,] 0.0625463094 3.8144211089 0.732997503 0.462586189 0.2249929889
## [674,] 0.0710041313 1.5784446178 1.926693602 0.655287857 0.5364981631
## [675,] 0.7386853027 0.7712930624 2.201500031 0.112117955 0.1481535812
## [676,] 1.0788041721 0.5347887358 0.420583755 0.120475326 0.2869075288
## [677,] 0.9110473433 0.2435321970 0.095519396 1.408040729 1.9951385300
## [678,] 0.3612217310 0.9515456054 1.378373951 0.867805129 -0.1386272672
## [679,] 1.2348851126 0.5387286368 1.215018325 0.648367478 0.3336980784
## [680,] 2.1189136778 0.6293952977 1.552784912 -0.163380310 1.2412051326
## [681,] 1.3449213927 -0.2073974015 0.395314075 2.122768352 -0.3032778257
## [682,] 0.3695825030 0.8974141200 3.287124727 -0.107970425 1.2371603728
## [683,] -0.2689251884 2.9627470971 1.750896503 0.698540018 0.8882957445
## [684,] 0.5258898205 0.5376883823 0.078165172 1.157208446 0.3814219992
## [685,] 1.8883614565 0.6522008091 1.731384433 0.556738782 0.4510465254
## [686,] 1.7506519038 -0.4209551058 2.923552834 0.197146694 5.1569333055
## [687,] 1.8047554398 0.1284470647 0.177839612 0.322468650 0.8514090513
## [688,] 0.2522288971 0.5418366637 3.158698174 0.998849103 0.3138037877
## [689,] 2.0062822479 1.8776806951 0.130537664 0.765015990 0.2091384730
## [690,] 1.0101899654 0.3259829099 0.211908906 0.961889207 0.3684283826
## [691,] 0.1908773253 1.2923900856 0.906269410 1.450099770 -0.0401488575
## [692,] 0.5364317662 0.0891149762 3.560954948 2.411029996 0.4683601537
## [693,] 0.5127584581 1.5897472020 1.672961763 1.883911858 1.8272655903
## [694,] 0.6987729590 -0.0017509246 0.447901050 0.288162339 0.0594884390
## [695,] 0.2414446850 0.6239192208 1.418981305 1.030440601 1.8026358390
## [696,] 0.4983851413 0.0556926231 1.164302124 0.391262845 0.5612570259
## [697,] 2.7920802701 1.9220525970 0.237533557 0.782927835 6.2613167295
## [698,] 0.4108974570 0.9675646663 1.950237176 0.943032042 0.9322376485
## [699,] 0.4011115756 -0.1476653048 5.180169220 2.311164818 0.1576557861
## [700,] 0.9548096626 0.0632434950 1.260083358 0.778322724 0.8261921669
## [701,] 0.2612508717 1.4378524742 0.543760792 0.584202059 0.5380780818
## [702,] 2.1903309089 1.0238641357 0.323528822 0.620373601 3.2238868712
## [703,] 5.0614064971 6.9035993020 -0.093309243 2.234772567 2.0108087862
## [704,] 1.4555216377 2.3118110652 0.531781596 -0.074119281 1.2528827966
## [705,] 0.3664599470 1.7462574048 1.286890185 0.815556988 0.7659146004
## [706,] -0.0656294894 0.9566792547 2.436786327 1.444193529 0.2137953026
## [707,] 0.6296528619 0.1855728379 -0.351197164 0.066067794 0.1926222485
## [708,] 0.3465854778 3.4585692855 0.366210541 1.724677721 0.5934479044
## [709,] 0.0459553282 0.9517298249 0.220300942 2.623171192 1.2632604295
## [710,] 0.4661458236 2.0518533341 0.237726129 1.335917532 3.5590251790
## [711,] 1.6037448971 0.2530927586 0.293145968 1.252721218 0.7101546312
## [712,] 1.8678338141 1.3804605667 0.582487080 0.180178291 1.1796723070
## [713,] 0.6322128236 0.5179365682 -0.232116547 1.342875503 0.9452284093
## [714,] 0.2822484884 2.2032274253 0.546375327 0.240423385 0.6031818495
## [715,] -0.0590213051 2.4315965864 2.264469167 0.747979907 0.8862286321
## [716,] 1.2957917393 1.0801698131 0.168470814 0.667468644 0.6541774062
## [717,] 0.6662882628 1.6714356840 1.356356039 0.228828136 2.5038900877
## [718,] -0.2826686624 4.5963282746 1.236709074 -0.290760656 1.8792903977
## [719,] 0.1500893180 0.0367912526 0.171125518 0.211125116 1.7650074859
## [720,] 1.3500715055 1.3861010616 0.909413365 0.981970605 3.4089491680
## [721,] 1.1876494826 1.1836553044 0.603182300 -0.102675514 3.3855797691
## [722,] 0.0025632955 2.0867092292 2.241244921 0.552334327 1.9197492096
## [723,] 0.2092064444 2.4144651141 1.549228304 0.008887515 0.2180802976
## [724,] 1.5163945341 0.1654330611 -0.202128681 0.991723003 2.5027852440
## [725,] 1.2242616797 2.1602568128 1.411738905 1.268455998 2.7866855348
## [726,] 0.0422081240 1.4676243491 0.278484276 1.507042763 2.2039879569
## [727,] 0.9655652691 0.4785907557 -0.064570065 0.567322134 1.0976196987
## [728,] 1.8022644138 3.7066067107 0.494109709 2.270078512 0.4791331123
## [729,] 0.9505189536 0.4685628602 1.613552020 0.367850480 1.3973175636
## [730,] 0.4296735289 0.2357834654 0.037161965 0.396981850 0.8377298149
## [731,] 0.1298214536 0.2751916721 0.437611144 -0.224324203 0.1597014534
## [732,] 0.8786518462 1.1240702176 1.356500445 0.914211251 -0.1334404898
## [733,] 1.1704867880 0.9672865044 0.235310565 0.907386231 1.8754352412
## [734,] 0.8519963489 0.8578478565 0.217974942 0.368073318 0.3607450662
## [735,] 0.6537459002 2.2601128391 2.924174651 0.387971843 -0.1076288466
## [736,] 1.3392399017 0.7892304458 0.023777085 1.148587363 1.0229700446
## [737,] 4.5531860276 0.5818818967 0.111287484 10.159168620 3.3049200829
## [738,] 0.2003959123 2.1291714612 1.004488008 0.439216119 0.7927126164
## [739,] 2.5493609542 0.4530605770 1.625480850 0.308358903 0.0345150506
## [740,] 0.7393404050 2.2418427531 0.023890070 0.432898162 0.7134284567
## [741,] 0.2893660784 0.0803946069 0.317782062 2.916871455 0.6287170464
## [742,] 0.3512585759 0.8802317994 0.750736896 1.400491954 1.1044062679
## [743,] 0.0832474946 3.4193996862 0.738467800 0.034905874 1.0343236452
## [744,] 1.6221987331 1.6929961404 0.143432201 0.833111024 0.5309482296
## [745,] -0.1596158072 0.7949514039 0.196154108 0.200453945 -0.0699915984
## [746,] 1.1703633790 0.6758585473 2.125653687 2.040605386 0.4983953098
## [747,] 0.8803359822 0.5116899880 0.882264642 0.015003149 1.0412470913
## [748,] 0.1006477083 -0.0521571502 2.010580378 0.956168937 0.5701298032
## [749,] 1.1530150475 0.1881236228 1.056132617 -0.045584013 0.8571833248
## [750,] 3.1519965380 0.5348742917 0.014034852 2.586793315 -0.2505896469
## [751,] 2.5923087867 0.4051185851 2.222842868 3.291175792 4.1118766840
## [752,] 1.0007131085 0.5374062082 1.420634265 4.246552744 -0.6366755259
## [753,] 0.2190620365 -0.0626093796 0.193123946 0.123142269 0.4630997678
## [754,] 0.9784965004 0.6619024590 -0.268483305 0.884885725 -0.0614734780
## [755,] 0.2680456285 0.2201740329 1.086919666 0.256564672 0.2844176487
## [756,] 0.7485303177 0.2143632082 0.834018152 3.226854408 -0.2210895294
## [757,] 0.5343692042 1.2778364058 1.747800945 3.959860956 -0.2649945827
## [758,] 0.2946193767 3.5496874324 2.719045641 0.367757960 0.7333225195
## [759,] 0.9875160586 0.2506895907 0.657751688 0.001078894 0.4730445590
## [760,] 0.1275119463 1.4136760030 2.252293745 1.716768863 1.3737917913
## [761,] 3.4673690255 3.4299116551 0.975307381 0.801194292 0.0286992172
## [762,] 0.1265726028 0.9434404894 1.099884530 1.924240279 2.9869215680
## [763,] 2.2860073128 1.1384109549 0.699205256 3.275777178 -0.0342615325
## [764,] 1.0577630739 0.9641865805 0.954004768 2.934079161 0.1406418869
## [765,] 0.6079493967 2.0110650476 2.640004859 0.538992213 0.6830907120
## [766,] -0.1536080985 2.3753952750 1.436359936 -0.126205876 0.0705421908
## [767,] 1.2961377207 1.2258527264 2.729075489 0.750696008 1.4366734368
## [768,] 1.6354461773 3.3544000236 0.529833776 2.579197598 0.2339146830
## [769,] 0.4322087354 1.1948981413 0.235859968 0.435634614 1.4534410563
## [770,] 1.1300237536 0.9114553239 0.880990741 2.218251626 0.2961337831
## [771,] 2.2052863888 0.8122295470 0.547948864 1.557178368 4.5619961175
## [772,] 1.0729978869 1.5880588666 0.397950432 1.742928334 1.5567385538
## [773,] 0.3818759040 0.2050128414 0.918247624 1.541523297 2.1014308838
## [774,] 3.6942740766 1.4502041666 0.196211668 0.642876631 0.4791831126
## [775,] 0.1720668339 1.1519689646 0.504837472 0.543606531 1.1544209142
## [776,] 2.8626304154 2.5502606213 2.711270037 1.563341721 1.5799484783
## [777,] 1.8941508254 0.9105519857 0.633413191 0.477907683 1.4977408945
## [778,] 0.5063916390 0.2884844844 0.786857108 5.543430463 0.8351955246
## [779,] 1.7782405396 6.5269713545 0.385969481 1.093281467 0.8580668243
## [780,] 0.1000247250 0.0180046351 0.303200802 0.465967030 -0.0027875563
## [781,] 1.4145866329 0.2761372844 -0.179129339 0.487727292 2.6284652133
## [782,] 2.2079276982 3.1732256961 2.436598175 1.083891946 0.5700669086
## [783,] 0.8129604679 0.4705296460 1.277919161 0.903726558 8.2879643480
## [784,] 0.0189440596 2.0324903692 1.991215408 0.330507750 4.5983435827
## [785,] 1.1699541285 3.9685670201 2.303114270 1.093499698 -0.1141995742
## [786,] 1.5270520404 0.1143566293 1.356084130 0.175014301 3.5554420699
## [787,] -0.3653440114 -0.1867198300 5.403944382 1.244996523 0.6367969184
## [788,] 1.2884279628 1.2888123395 1.366288589 0.053255582 0.0406754959
## [789,] 0.7981332512 0.1958742292 0.268480147 1.302861858 0.7393862649
## [790,] 1.4108110374 2.7220513078 2.241554472 0.713410309 3.4407966224
## [791,] 2.5995084051 6.9406004680 0.183845101 3.236582602 -0.0007801916
## [792,] 0.1902823281 0.8236831590 0.178562987 3.488677446 0.5562824043
## [793,] -0.0907293244 3.4042264149 0.977515256 0.167238688 0.1491600242
## [794,] 1.3555052049 0.3906325565 0.431283240 0.455886000 0.6874045402
## [795,] 2.8248347990 1.6536829426 0.960487939 1.041631416 -0.0326447610
## [796,] 1.5026162615 0.4011520101 1.288083988 2.164506010 0.0654170501
## [797,] -0.2924639853 -0.0846666779 0.481516179 1.292967037 1.8911380006
## [798,] 2.0624233972 0.6673500489 0.874895379 0.504268828 0.5526691692
## [799,] 0.5068796208 0.5062401886 0.377800584 5.229209854 0.4230711918
## [800,] 0.0230390922 0.5950872702 0.999926635 0.430254929 2.1322693875
## [801,] 0.8348814019 0.4023536590 -0.301871246 1.925917674 0.9570077341
## [802,] 1.2520346070 1.0371584640 5.130659094 0.681375806 3.3459967801
## [803,] 3.2031836066 0.8129441450 3.483141259 0.946156501 2.8332298165
## [804,] 0.6564527886 0.5889248006 0.069669167 0.810508688 0.5249394797
## [805,] 2.2859109282 0.7779599057 0.161494748 0.696660366 2.6572715586
## [806,] 0.7358308275 1.0798191529 0.060179397 2.180967662 2.4946705744
## [807,] 2.5794423521 1.3111117215 0.278111070 1.139681942 0.9459274699
## [808,] -0.1909957983 0.8898605758 0.202287199 2.239038509 1.3550819488
## [809,] 1.0701351500 -0.0393936889 2.038453495 3.688130573 0.7533770075
## [810,] 0.7996356848 0.1045223385 0.634256678 1.317063031 1.5820715651
## [811,] 1.0360546156 1.5637637314 0.036407098 1.583170259 -0.1482190113
## [812,] -0.1163201184 -0.0259229877 -0.072078593 4.218539143 0.1894235634
## [813,] 2.6098670111 0.8006572584 2.398098934 0.189177163 0.7225071112
## [814,] 1.9475058354 1.2711580545 1.193583615 1.705255674 -0.2332156725
## [815,] -0.1488203176 0.3599112199 0.734313999 0.305696066 2.7227430480
## [816,] 0.5529617354 2.2867818258 1.118153814 1.044495971 0.8804799437
## [817,] 1.5666670614 0.2519250525 1.150673189 -0.001946236 1.1169148185
## [818,] 0.5952679334 1.4182174686 2.149840184 1.494363566 0.6416965474
## [819,] 2.7238341283 1.1807509500 -0.027602671 -0.256927721 1.0807293153
## [820,] 0.5625671471 1.2004407370 2.408799568 1.777770370 2.5695500190
## [821,] 1.7911214521 0.3255983917 1.355658078 3.402953360 0.1514834259
## [822,] 0.1948077218 1.9954095879 0.776765797 0.886673123 0.3245632256
## [823,] 0.6391653046 1.3892458306 0.227993666 0.231907782 0.3773518037
## [824,] 0.2020682223 -0.0257191531 0.539595495 0.307523832 1.4419900827
## [825,] 2.9053033343 -0.0070977713 1.876039400 0.378663696 -0.1517280640
## [826,] 0.5834445519 0.5827896084 0.637335761 0.436662163 1.2066788392
## [827,] 0.2527504171 3.8203473626 0.514143968 0.258694168 0.6444591877
## [828,] 0.5181707795 1.1694322976 0.209440122 0.499207843 0.5301114110
## [829,] 0.5900996639 0.3239858397 0.026033887 0.596244500 0.3592911196
## [830,] 0.0637225227 2.5837813772 0.250758722 -0.069238360 0.8565596178
## [831,] 0.5174948388 1.7560640770 1.100272841 2.927714441 0.7815295500
## [832,] 1.3476671066 1.1570815868 0.238494177 0.498093227 3.1768912205
## [833,] 1.8652445638 1.3822289263 0.106327190 1.422694194 0.7254406337
## [834,] 0.2375757951 1.4328817727 0.886360596 1.280336373 1.5135802651
## [835,] 1.1548122980 0.5770313991 1.316301499 2.386900506 1.8236974407
## [836,] 0.3769891183 1.6819973422 0.785325511 2.044860230 0.7191518891
## [837,] 2.0012277106 0.5439399375 0.412208088 0.460223257 0.9097876105
## [838,] 0.2052923463 1.9002458457 0.623451107 3.148729138 3.3418534271
## [839,] 0.8946980175 3.9063283762 0.186072873 1.131991706 1.8720313480
## [840,] 2.4137270751 5.2149046794 5.426700010 2.575991971 2.5664423968
## [841,] 0.1237343266 0.2345054560 0.729518015 0.241430002 1.0852547875
## [842,] 0.1349092927 0.0689731539 2.247973339 0.271763343 0.2258039514
## [843,] -0.3861846508 -0.0578246676 0.323894308 0.376808892 0.1800998703
## [844,] 0.9355093825 1.3276814374 0.851215587 1.265110120 0.9898287175
## [845,] 0.6225958589 1.8034080360 0.481478041 -0.284833780 0.8864946084
## [846,] 0.2722691107 2.7067956326 0.388586164 4.534272505 0.9379696360
## [847,] 4.3526108283 2.0979957020 -0.195000933 0.252978971 -0.1805448856
## [848,] 3.2468584731 0.5508860134 -0.009167378 0.120163196 0.1286532712
## [849,] 0.9097339480 0.2941258149 0.133426960 2.142276143 1.5953232826
## [850,] 1.2789243565 0.6527119414 1.184539032 0.135723576 1.4986268232
## [851,] 0.9481758915 -0.0095872744 0.394635350 2.735426197 1.6896982880
## [852,] 0.2744895292 0.5103553911 0.219789590 0.577561446 1.2836615107
## [853,] 0.3680780901 1.1789552770 1.367872960 -0.001834057 1.2559772138
## [854,] 0.0249593517 1.1054822271 0.554610612 1.986768176 0.6642986454
## [855,] -0.3892842028 2.9880292906 2.167212258 1.692017114 6.1555983873
## [856,] 0.5908604072 2.7151021185 1.475862110 0.407935658 0.1443173135
## [857,] 0.4167999861 0.4134400776 1.693392043 -0.133281102 1.5921017645
## [858,] 0.5593000305 -0.1451432315 0.592669305 0.333378389 0.5714943913
## [859,] -0.2069963782 0.8371859216 1.156496969 0.977328275 0.1062956685
## [860,] 0.5894177710 0.8604786427 1.521666945 4.060780051 3.4561831934
## [861,] 2.1990113926 0.3966330921 0.055116173 2.242398630 0.2605680700
## [862,] 0.9016667032 -0.0006204587 2.495944076 0.469806828 0.5603374784
## [863,] -0.0423694623 0.5648199839 0.546211585 0.125481203 0.4420178163
## [864,] 0.7117584866 0.5164444521 0.799236899 -0.179673203 1.3577351605
## [865,] 0.1445770392 1.1300889981 0.530607278 0.708555305 0.6454414421
## [866,] 1.2724062611 0.6538678182 0.559012370 0.226964836 0.4717421479
## [867,] 0.5044618460 -0.0816372977 0.727667085 0.084847319 0.8303344441
## [868,] 0.3431782904 -0.1428361374 1.320818926 0.691181471 0.8132668566
## [869,] 1.2678641229 2.0725472988 0.265058571 0.237177920 0.0258604173
## [870,] 1.6503344015 3.8673300073 0.174716871 0.556462570 0.4330385820
## [871,] 0.5776995834 0.1985185125 1.938303991 2.061670625 4.3213514637
## [872,] 1.3810344096 0.2419142242 0.405504843 0.917833235 1.4944257045
## [873,] 0.8882867903 0.0702308735 0.681782726 0.625223991 -0.0983326833
## [874,] 0.0665077765 2.3078966375 1.587817794 -0.078381210 0.1347478226
## [875,] 0.1478835324 0.1943659804 2.154873787 0.452183457 0.7481828269
## [876,] 0.4381854720 1.7197195802 0.278013780 -0.185942709 1.4934294252
## [877,] 0.2340192510 1.4792285853 0.633207756 1.671053786 0.1393640324
## [878,] 1.2877311745 0.2582947012 0.523886245 1.346977077 0.1734657111
## [879,] -0.0643966386 0.9849509126 -0.219529595 0.604526281 -0.0553821317
## [880,] 6.4986492677 -0.0703065987 3.718525828 0.471708886 0.5026881632
## [881,] 0.2014182600 -0.1956294248 4.250636374 4.675378319 1.4922804786
## [882,] 0.3731709640 0.8530171456 2.790130562 0.052976604 2.3798925700
## [883,] 1.3460816118 -0.0140180906 2.115048676 2.294456127 0.2329834694
## [884,] 1.0123575735 -0.1072201810 0.140928639 0.310847614 0.5385466535
## [885,] 2.1183134965 1.7891319142 5.061699715 2.762056374 0.4832557156
## [886,] 0.7038399546 0.1750322869 0.134677713 0.926583303 0.0246873722
## [887,] 0.0533500205 0.4927530847 1.285068321 1.503488048 1.8667396215
## [888,] 0.2755895974 -0.2108115412 0.076331843 1.744519904 1.4118425840
## [889,] 0.4891336488 0.5211272495 0.665617632 0.203945867 0.1081577404
## [890,] 2.3276355302 0.5272503532 0.117871130 1.319756712 2.9241187042
## [891,] 0.7506910667 -0.0774854632 0.470536337 0.581099588 0.6913743969
## [892,] 0.9904297912 -0.0171020674 1.140931020 2.011199689 0.0895951210
## [893,] 0.0345622202 1.1150944338 0.339170926 0.850448110 -0.1153085326
## [894,] 0.0359935881 0.2889200480 3.112986079 1.508299268 0.7591502880
## [895,] 0.6986169282 -0.2080769042 0.688853200 0.748468298 2.8496422289
## [896,] 0.7920224377 1.4762746577 -0.011533133 0.871344882 0.0994684191
## [897,] 0.2487711176 1.2832312420 1.722494957 -0.154996380 1.3966724206
## [898,] 1.3937659145 1.2636593366 0.450212299 1.151389913 0.8128885629
## [899,] 0.0656063250 0.2516932487 0.209170635 -0.008790003 0.9617806185
## [900,] 0.3788477632 0.4373127831 0.154069267 0.433685243 0.3623718775
## [901,] 1.1162148837 0.7388431474 0.943592808 0.879550754 -0.0229861563
## [902,] 0.6310182010 0.2449754782 0.743546119 0.847155701 0.7844201871
## [903,] -0.1813458866 1.8374208066 0.373410373 -0.249059277 -0.3156409326
## [904,] 0.7352528002 0.6180599548 1.124545837 0.966622330 0.1197291111
## [905,] 1.1138959913 0.0796172457 0.726535444 1.927392198 0.0926907102
## [906,] 0.6813086148 0.9325334678 1.986274059 0.276193874 2.1206992039
## [907,] 0.0040645085 1.1858523287 1.126785733 1.838783369 0.6915851179
## [908,] 1.6787229397 0.7711245251 0.396606549 2.122230294 1.3222644045
## [909,] 0.5203080755 0.3999669827 0.071318552 0.248339421 0.6245152153
## [910,] 0.8328324209 1.1820433963 -0.215101219 1.155326629 0.3298523690
## [911,] 3.6129867697 0.6742010537 0.014972888 0.748568733 3.9400884789
## [912,] 0.0553348172 1.4668504755 1.273443214 0.771546340 0.2718694335
## [913,] 1.5367626642 0.2190583806 3.438838112 0.794276976 0.6113879193
## [914,] 1.6415827266 1.1143078235 1.622054698 0.538527286 2.6367907877
## [915,] 0.5425318331 -0.0179732935 -0.097155641 0.615119118 -0.0121596918
## [916,] 1.2786787688 0.7293892836 1.897562249 4.930526531 8.3398721864
## [917,] 0.5506037807 -0.0999842391 2.728371572 -0.107186838 0.3876571737
## [918,] 0.8099001071 -0.6490769951 0.617381703 1.826139646 0.7372019348
## [919,] 0.2196721489 0.7390635607 0.194377725 0.702848228 1.8171671210
## [920,] 0.6759174610 0.7795179148 0.364212733 0.740593921 1.1016918585
## [921,] 0.5231618102 0.4131937248 1.838567380 0.770492350 0.3046182864
## [922,] 1.0836245683 2.5621969376 0.449058643 0.598009425 0.5676349520
## [923,] 0.7686051944 0.1486231808 -0.037058375 0.592449570 0.5377193426
## [924,] -0.0308582925 1.9690441250 0.978652365 -0.037416691 1.6128271071
## [925,] 3.9942148800 0.2107157358 0.148380607 0.793077845 1.9458822988
## [926,] 3.1027019095 1.7600102886 -0.322512622 0.933273346 0.7111974423
## [927,] 0.3059963865 1.3495261806 1.277843829 0.320479358 2.9269898114
## [928,] 1.8611047407 -0.2050255628 -0.014356793 -0.031457434 0.9489700361
## [929,] 0.0177637743 -0.1405138621 0.574355038 0.365712960 0.7392783249
## [930,] -0.1142315827 0.2625794220 1.439609603 3.523180615 2.1949217224
## [931,] 6.3477881647 0.4788658780 2.039496498 0.848472441 0.6786045462
## [932,] 0.7962804407 0.4740565642 0.090811334 0.080659599 -0.0643365018
## [933,] -0.1215665941 0.2601310614 1.188732801 0.084135552 0.5027608483
## [934,] 1.6175169780 1.7006020975 -0.134897146 0.718355081 1.3983021256
## [935,] 0.7228984541 0.5032167620 0.348305992 7.003632508 0.3116332116
## [936,] 1.3663799723 1.5260202352 0.553171145 0.108100684 0.0270602498
## [937,] 0.2860401885 1.2314254197 0.691232805 -0.151008944 2.6248561048
## [938,] 1.1693142547 0.4624600113 2.953706117 0.957209432 0.2851808985
## [939,] 0.8536335489 -0.2658406369 -0.160987723 0.260415363 1.9066233404
## [940,] -0.1930199885 1.8231665768 0.792581400 1.366354177 -0.0184102512
## [941,] 0.9608425487 8.5587464298 0.853588960 2.030279058 0.8649472381
## [942,] 0.7167440365 0.6660827071 0.115834896 1.289342665 0.5678395500
## [943,] 2.9508162746 1.1543661913 1.478387239 0.243845627 1.1686275972
## [944,] 3.7115770044 1.8576424227 -0.152130970 0.324460977 0.9727013767
## [945,] 0.4586230975 2.3496274305 1.899204765 -0.060245689 0.9734010606
## [946,] 0.0584200833 -0.1798403829 0.196367611 0.420744288 1.7402924560
## [947,] 1.5696946239 0.6443676133 0.509498910 0.381669775 1.7648095119
## [948,] 0.2856898106 0.6419538623 2.880055967 2.224600120 1.6030366656
## [949,] 1.5681204311 0.6729804561 -0.006969424 -0.010584874 0.0673519065
## [950,] 1.5473736879 3.8344335579 0.691310470 0.456047584 1.4225746999
## [951,] 0.3724693924 0.8797569538 -0.162087596 0.498346983 0.1392819545
## [952,] 2.7100999687 2.0003254943 3.510005365 1.116802216 1.7013423949
## [953,] 1.7636559191 0.0929840756 0.163300805 1.201375021 0.1425632301
## [954,] 0.9171300212 -0.4821951829 0.549343823 0.757300371 1.5870176861
## [955,] 3.7953174573 4.9950083758 2.799988493 0.919200975 0.0797304845
## [956,] 0.4196065486 0.0594501921 2.485623413 0.404869265 0.4328184868
## [957,] 0.3389352694 0.2135964600 0.415109206 1.548323108 0.9409364132
## [958,] 0.6090206350 -0.0867589725 1.400548707 -0.159778880 2.1759421338
## [959,] 1.3534859661 0.5858814639 1.159396274 -0.008503290 0.5750999616
## [960,] 0.6889071333 3.1698012480 2.751938288 1.035929874 -0.1494223370
## [961,] 1.2882499759 0.1308812110 3.500843206 1.667289221 1.3979249884
## [962,] 0.2577474878 0.4100520488 1.417168563 0.301247699 0.5063852904
## [963,] 1.1218294596 0.7956216278 0.541374044 1.378818316 0.6253320576
## [964,] 1.0082500837 0.4513470533 0.161981502 1.360322190 -0.2307029059
## [965,] 0.6048066942 0.4106273981 3.102918985 0.941497957 0.5631934039
## [966,] 0.2154063825 0.3411579486 0.381012748 0.008159854 1.4273017267
## [967,] 1.6182987328 -0.2565442952 1.268106221 1.596053765 1.7296327503
## [968,] 1.2792225057 5.6166106139 2.262065205 1.429980548 6.0858256227
## [969,] 1.6134530687 2.7060480778 0.212278788 1.484462561 0.3491779768
## [970,] 0.3946663291 -0.0849647096 -0.121780934 1.807681948 0.8650151991
## [971,] 0.6695552320 1.0557364503 0.332585434 1.188805842 2.2174927204
## [972,] -0.2808270924 0.1187759620 0.288332505 1.285313912 0.9962276486
## [973,] 0.3558108894 1.3092014433 0.885225270 1.339782418 0.0368311346
## [974,] 0.5630732242 -0.0339007299 2.994268801 0.591215350 3.4877722770
## [975,] 0.6639024152 -0.0573599181 0.003713450 1.400072462 0.5354054130
## [976,] 1.0394366013 0.2276801727 -0.064772635 -0.124145239 0.7075164003
## [977,] 0.8325664291 0.0109883360 0.107444539 5.182736776 1.1176656650
## [978,] 4.9460663510 -0.1770568146 1.825646739 0.725824358 0.1826518718
## [979,] 1.2296661511 0.6925387997 1.307403123 6.617068182 0.3416476673
## [980,] 0.5867581199 0.3791149177 0.049758831 0.060192317 0.5203150571
## [981,] 0.1210431617 2.4668159690 0.440051095 0.381700325 1.1305936832
## [982,] -0.0424567569 0.9176610649 2.972562497 1.299812589 4.5860599194
## [983,] 1.1199641217 -0.2916205759 0.082031819 1.687779552 1.8272838093
## [984,] 1.1508058406 -0.0500255274 0.430196842 1.412204508 0.3947114711
## [985,] 0.9839827948 0.2490480506 1.218517162 1.305887023 1.7420776949
## [986,] 0.6253901402 1.0087257875 0.189298433 2.129418585 0.1275911972
## [987,] 0.0239923014 0.5125437660 0.631106531 1.123867979 0.7910380429
## [988,] 0.0745923484 1.0584455254 0.336691140 0.885466140 0.9001598087
## [989,] 1.3080204193 0.3810902551 1.180359702 -0.074689803 0.7478007012
## [990,] 1.5809851137 0.4889945952 0.479909896 0.509602044 0.7617443235
## [991,] 1.0499080874 0.5084886443 0.864443144 1.595885082 0.1601012679
## [992,] 1.0230909114 0.9134424806 1.937816940 0.426601324 -0.0810008677
## [993,] 0.0432798059 2.3212032567 0.587060051 0.630175328 1.4426987850
## [994,] 2.7112361540 1.1136669368 0.374961860 -0.155553727 0.7010075936
## [995,] 3.0683300666 0.4969290755 0.013703828 0.908796329 0.2508477748
## [996,] 1.8926352686 0.1415544360 0.191589326 0.118988549 1.3280206325
## [997,] 2.1577314845 0.6503373444 1.429536585 1.325682032 1.3753461765
## [998,] 0.3908603654 0.7745483311 1.393843990 1.676403170 6.3324467801
## [999,] 0.0766322681 0.7935669580 1.707705943 1.397107848 2.4394959744
## [,6] [,7] [,8] [,9] [,10]
## [1,] 1.3151685846 2.473879371 1.927612e+00 0.6924370643 4.7815747161
## [2,] 0.4884188840 0.523395371 5.768997e-01 2.2885044926 0.0219193121
## [3,] 2.1040394307 1.329002943 2.524405e+00 1.4467660092 0.9294676626
## [4,] -0.0112105685 -0.011548184 7.068133e-01 3.3630236319 0.1226866769
## [5,] -0.0661807474 -0.155150847 7.234826e-01 2.3278110177 -0.1275195548
## [6,] 0.0953827763 1.892089955 8.925631e-01 0.8795468379 0.0709554277
## [7,] 0.4496779932 0.841918835 4.712815e-01 0.4365837437 0.5432338095
## [8,] 3.7432944852 3.127972706 2.157621e+00 0.1384945303 0.2741131240
## [9,] 0.0503161515 0.115085382 2.670218e+00 3.7897934332 0.2197900650
## [10,] 0.8280300919 0.540056720 -1.365295e-02 1.1885690701 0.0572364882
## [11,] 2.0346795056 2.315546334 4.119921e+00 0.0479775283 0.5165281555
## [12,] -0.3583860476 0.612147548 -8.979572e-03 0.8092326206 0.9823099607
## [13,] 0.7202841067 5.384255096 3.019309e-01 0.7852423194 1.7351637677
## [14,] 0.6004283684 0.480633127 -1.649184e-01 0.6542765013 0.5473131793
## [15,] 0.5988112891 0.101200096 3.828075e+00 -0.2878143553 0.2116583314
## [16,] 1.2301492469 1.620449322 3.529032e-01 1.6120101575 0.6237788852
## [17,] 2.9418470668 0.816988300 1.160568e+00 0.9992891499 0.5302896006
## [18,] 2.6600804237 2.085686860 1.147045e+00 0.6440365908 3.3143981523
## [19,] 1.0697329775 1.045513394 1.652913e+00 0.2954879996 0.1690948542
## [20,] 3.0724212282 0.613313662 4.437370e+00 1.6728999324 -0.0151055927
## [21,] 0.2600980899 1.219774991 5.207380e-01 0.6044819040 0.9375487873
## [22,] 0.2083023701 2.403908833 1.567479e-01 0.2032069989 0.8677078992
## [23,] 0.7672723000 0.564313981 2.692716e-01 0.5185904448 0.5192169920
## [24,] 0.7211721229 3.114849198 1.590760e+00 3.9377454390 0.2226997099
## [25,] 1.1235152207 -0.044139422 1.753989e+00 0.5307068080 0.5574257041
## [26,] 3.4613967936 0.140562267 3.894596e+00 -0.0793250296 0.2844012961
## [27,] 2.9361908229 0.797868000 3.036669e-01 -0.0762857703 0.9018441373
## [28,] 2.5899449311 0.508921461 5.391967e-01 0.2466475765 0.8160655958
## [29,] 1.1884565530 0.482440421 1.024214e+00 -0.2076358543 0.6698916176
## [30,] 0.5538141332 0.986119311 -5.281031e-02 0.8847052002 0.2174004992
## [31,] 0.3933043791 0.988109040 4.330524e-02 0.4712213308 1.4677790415
## [32,] 0.9731133117 0.098593374 -5.961158e-02 3.8488626971 0.6484167363
## [33,] 0.5326203171 0.416263285 -3.369900e-01 0.1099712307 0.1157564530
## [34,] 0.1069514408 1.001756187 1.160532e-01 1.2353048412 1.4464259418
## [35,] 0.1171286530 2.724600365 1.938760e+00 2.4462852765 2.5022300949
## [36,] 0.2708720500 0.268143475 2.053203e+00 1.1500762157 0.2393957692
## [37,] 0.6274541922 0.762937124 2.259775e+00 0.1826981043 0.2594569945
## [38,] 0.0260076594 0.843715241 1.388240e+00 3.2837490982 0.6034949661
## [39,] 4.1003482536 0.283269591 3.339982e+00 5.8429508469 1.2673719253
## [40,] 0.7047724130 1.178443615 1.816264e-01 0.8003639529 1.1981182229
## [41,] 0.2088343670 0.017724882 7.401551e-01 2.2952305033 0.4594442360
## [42,] 2.3551833090 1.676272456 1.629215e-01 0.8009308798 0.8037305749
## [43,] 2.5050732376 0.034879130 2.135357e-01 0.0830959304 0.9157562744
## [44,] 1.1309801393 0.130260219 -1.880286e-01 -0.0086379109 0.9090509525
## [45,] 0.9920682601 1.399438278 -1.637668e-01 1.3521092743 1.5738517717
## [46,] 2.0343374929 1.680299947 2.705459e+00 0.4188256760 0.1553808509
## [47,] 0.3498161851 0.487722084 1.953102e-01 0.8752340688 0.7422392565
## [48,] 2.6542594073 1.430734167 1.248694e-01 1.4816726376 1.4469159455
## [49,] -0.0802845785 0.884602233 7.681333e-01 0.1799400505 0.1377179626
## [50,] 1.1115617129 1.021616906 8.384472e-01 1.7200227508 1.5157152719
## [51,] 0.3881021363 0.855004425 3.263933e-01 0.0778489426 2.4259861855
## [52,] 1.7348399843 1.843424443 7.409492e-01 0.3563550511 0.0455448567
## [53,] 0.0786757572 2.129717980 9.718148e-01 0.8901793689 1.2608421228
## [54,] 3.6491058081 4.690831541 8.086656e-01 0.2895150333 0.3239902463
## [55,] 0.6125488178 0.621069594 3.602210e-01 0.2762861908 1.5151348578
## [56,] 0.1049239548 0.464823487 9.025401e-01 2.6453115085 3.1938277738
## [57,] 0.8668306752 0.482317519 1.338494e+00 0.4877944387 4.0576099281
## [58,] 3.2060697767 0.255317043 6.331114e-01 2.4080509391 0.4048178353
## [59,] 2.0458101795 0.147747553 2.232393e+00 4.5251482681 0.9813450118
## [60,] 0.0998006177 -0.294102574 1.380637e+00 1.2875103584 0.3361107970
## [61,] 1.9483978720 7.285787099 2.167941e+00 0.2831952252 1.6636569237
## [62,] 1.3474080466 5.001036704 2.881811e-01 1.9645485732 1.2756632235
## [63,] 0.4344337008 -0.287450536 7.828067e-01 3.4390986189 -0.1016786682
## [64,] 0.1696833536 1.004506543 -1.499113e-01 1.8611639269 1.5912297844
## [65,] 0.0689002796 0.237578470 5.194236e-01 1.1701866538 0.1299701214
## [66,] 0.6014963585 0.030229989 1.054099e+00 0.3999694294 1.2060035780
## [67,] -0.0505530332 0.821573794 7.108699e-01 0.1619489046 1.5281285119
## [68,] -0.3476502619 0.062639665 4.007553e-01 1.7706344720 0.6363477528
## [69,] 0.6947496179 0.510536250 -1.446082e-01 -0.2643525669 1.2634613010
## [70,] 1.4489231601 0.738047777 8.258743e-01 0.4002573771 -0.2994228585
## [71,] 0.4225248008 0.316292955 5.791427e-01 1.0533660177 0.6479621999
## [72,] 1.7960316939 0.162160894 5.925282e-01 1.3187284721 0.2105608962
## [73,] 3.0298477692 0.890659873 -1.186637e-01 2.1320559799 0.6036442936
## [74,] 0.0455855013 2.197921275 1.481407e-01 0.6127815763 0.4942808231
## [75,] 1.4094609301 0.102032025 1.394383e+00 0.7296115777 1.5765508001
## [76,] 0.6064976763 0.283544579 3.797731e-01 1.0521411404 0.6899377809
## [77,] 0.2726483427 0.849902463 2.656451e+00 3.6480425247 1.6256431909
## [78,] 0.6962138966 0.483034002 5.788474e-01 4.2471720669 4.6497386485
## [79,] 0.8389128851 2.926245370 1.648165e-01 8.9355646361 1.4426876762
## [80,] 1.0456418938 0.069066634 5.851353e-02 0.2535662315 0.5985514292
## [81,] 1.5489131207 0.568009307 2.109596e-01 0.0474800009 -0.2273341628
## [82,] 1.4582426407 0.614536301 7.694590e-01 0.8398461388 2.4541147023
## [83,] 0.1951337553 0.843426282 1.360034e+00 0.0642298783 -0.1730038390
## [84,] 0.5415694303 0.744433915 1.903409e-01 0.7234353173 0.4779403117
## [85,] 0.4382621252 2.034281402 6.410049e-01 2.2558561992 1.9304612530
## [86,] 2.3999960908 -0.021289708 1.308885e+00 1.0515378314 -0.1743539816
## [87,] 1.5569639194 0.528956151 3.224858e-01 3.8543273553 0.1348226442
## [88,] 1.4845221649 0.846569223 3.091739e-02 0.1517813780 -0.0287578613
## [89,] 0.6781773382 0.109940620 1.783809e+00 0.4041950461 0.1525741671
## [90,] -0.2655877112 0.845316741 3.299398e+00 0.4801381131 1.8231511026
## [91,] 0.6991732837 0.418895827 1.371114e+00 0.2553012709 0.6362635935
## [92,] 1.2856228017 0.225873189 1.547198e-01 0.4028114763 0.6430519686
## [93,] 1.4630849761 2.157918602 1.028454e+00 2.2426258660 1.1146115846
## [94,] 2.6060763987 0.072254992 2.575910e-02 0.4382050876 0.9842497435
## [95,] 2.0829244047 2.805385609 8.056034e-01 3.5496650694 1.7706903020
## [96,] 0.2717641586 0.363189290 1.338850e+00 0.6813698014 0.6544558634
## [97,] 0.7734654696 1.457221694 4.721913e-01 0.4385434463 2.7124382699
## [98,] 1.1021849182 2.194363271 5.009698e-02 0.3058867382 0.0788345977
## [99,] 2.6847810203 0.074476080 3.029940e+00 1.2548288970 1.3510557997
## [100,] -0.0027534466 -0.143128258 1.602313e-01 1.0150069491 0.2134898496
## [101,] 1.0607218068 0.609544012 2.457055e+00 0.6638469351 1.0345938349
## [102,] 0.8979810632 1.540026776 7.086216e-01 5.0973144989 -0.0980141629
## [103,] 1.4872551248 5.201781521 -3.456841e-01 2.2378695874 0.1218403664
## [104,] 0.8750774785 -0.036488284 5.193587e-01 2.3378226677 2.2181861198
## [105,] 0.6384261054 3.587952138 5.974466e-01 0.6719077962 0.6454797922
## [106,] -0.1677394614 0.748442395 4.472943e-01 1.6985829725 -0.0850553106
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## [513,] 0.2295587462 0.095719305 -1.015083e-01 0.1100331250 1.3539465615
## [514,] 0.1100491954 0.035239818 4.063091e-01 1.3252936785 1.0468168636
## [515,] 0.2531871440 -0.004839714 9.815800e-01 -0.0189442296 1.3053208855
## [516,] 1.0668162186 -0.449299518 1.441318e-01 0.7198565087 0.3863294394
## [517,] 0.4812484240 0.038122784 5.096332e-01 0.1602021833 2.3323883534
## [518,] 2.1190343003 -0.022588202 6.364129e-01 2.7864502166 0.5344348518
## [519,] -0.0960816996 5.489942308 3.831747e-01 2.4848161847 1.4088079805
## [520,] 0.2382808423 -0.150549064 -4.725740e-02 0.5456703367 2.5471314661
## [521,] 3.7470249632 0.051944182 -2.335891e-01 1.7660693282 0.2791766147
## [522,] 2.0258437321 0.380730713 1.492397e-01 1.7433528870 -0.0610021651
## [523,] 1.9445014044 -0.069844661 1.243275e+00 -0.0021751434 0.6350652643
## [524,] 2.8969241888 0.319148531 1.150680e+00 2.3687612291 1.1254151501
## [525,] -0.2492949709 0.019829507 -3.604625e-01 0.1998732057 1.0599586033
## [526,] 1.4628354638 1.727262490 2.690925e-01 0.6588585228 0.6638846942
## [527,] 1.5608454466 0.119214498 2.387903e+00 0.4454651465 1.5235532263
## [528,] 2.2599638871 0.152831945 -1.570977e-01 0.4429693380 0.1459850835
## [529,] 2.8401965851 -0.131519075 6.080097e-02 1.4485873923 3.8006971533
## [530,] 1.3063654004 0.472685109 2.906993e+00 0.7712427776 0.6398878117
## [531,] 1.3710140765 0.266697055 3.181566e-01 0.4236539044 2.2807971560
## [532,] 0.9964823451 2.635925238 4.586601e-01 0.4132507275 1.5104204741
## [533,] 0.4204107069 0.333188927 1.036512e+00 1.3634181581 1.0715072466
## [534,] 0.0571867447 1.635285214 1.387386e+00 0.3764107645 0.2609980797
## [535,] -0.0425671810 0.511718557 1.191640e+00 0.7809244836 0.0478987196
## [536,] 0.6695796379 0.372915914 6.882299e-02 0.2766788816 0.4656542514
## [537,] 0.0782641175 0.048386067 -2.085179e-01 0.2337144926 1.6720735231
## [538,] 0.9780487487 0.762689843 2.306943e+00 2.1109591185 -0.3517375132
## [539,] 0.1380506717 0.433552851 5.247788e-01 0.5146948260 0.0230097939
## [540,] 0.3482793056 0.382635199 2.713038e+00 -0.2764321912 0.6196877482
## [541,] 2.9410469680 5.731977609 9.063675e-01 3.1867805577 2.2210410078
## [542,] -0.0413812859 0.753605866 9.058710e-02 0.2315684889 0.8682632716
## [543,] 0.5528915075 0.938139947 -2.303234e-01 0.2101835222 1.1186164186
## [544,] 0.1333084236 0.090883318 1.353647e-01 0.2498200893 0.8793795171
## [545,] -0.2466310446 0.014907595 1.317587e+00 0.8807096164 3.5114060348
## [546,] -0.0914230814 0.157935925 8.201435e-01 0.2143864591 0.5521815840
## [547,] 0.5540899419 2.252639904 2.363315e-01 0.4937760641 0.4779570229
## [548,] 0.0253980944 1.022739151 7.433311e-01 1.2606004412 0.1857872737
## [549,] 0.3604550569 0.372933398 1.941820e-01 0.4197336568 0.1303547600
## [550,] 2.4925927970 0.894490092 -2.344840e-01 3.6438316994 2.8989611011
## [551,] 0.5280459393 0.933777106 1.235635e+00 1.2324129729 -0.3093124550
## [552,] -0.0427840936 0.006185315 -2.137505e-01 -0.0311728312 0.3747843013
## [553,] 0.4730345216 1.046113088 1.135548e+00 1.8255565503 0.6385611750
## [554,] 0.4825009463 1.110139894 1.782598e+00 0.0045337108 0.2937786933
## [555,] 0.1512916303 1.232364788 1.808328e+00 0.7855511616 0.5925819728
## [556,] 1.5803124932 2.175570386 1.929567e+00 1.1991097588 2.2538837416
## [557,] 0.5717096976 -0.051367285 1.461813e+00 1.0526150898 1.5443642438
## [558,] 0.7617383885 0.611491747 1.726239e+00 0.0183083864 0.2958307367
## [559,] 3.8542352083 0.017082713 2.277770e-02 3.0886437164 0.4810876794
## [560,] 0.1079325717 0.332691246 2.610406e-01 0.1455363482 0.1329396392
## [561,] 3.1426448565 1.080630569 5.155543e+00 3.3398493452 0.8539281618
## [562,] 2.0603485923 0.265118409 4.850053e-01 1.1988138591 -0.0356418789
## [563,] 1.4209696669 4.846623806 1.077002e+00 0.0469432417 0.8015811442
## [564,] 0.7823720609 0.066442309 7.504015e-01 1.0882580577 0.6921626564
## [565,] 0.3915616924 0.399635476 9.030948e+00 0.3532983086 0.1763928864
## [566,] 1.2277941572 1.708445884 1.939774e-03 0.4124225048 0.3665453744
## [567,] 0.1979907916 5.554167199 3.489224e+00 2.4248028244 0.0669746944
## [568,] 4.7006031392 1.355289942 6.531982e-01 0.3263070465 1.4045314399
## [569,] 0.8163156608 0.759498177 1.529616e-01 1.3526017610 -0.0225978491
## [570,] -0.3712985293 -0.046772413 1.936352e+00 -0.2740929584 1.7258893039
## [571,] 1.4036648058 0.450569676 2.644486e+00 0.2843984656 2.4493495087
## [572,] 0.3027441699 0.270667415 4.972250e-02 2.1288061641 2.9112305365
## [573,] 1.1309362956 0.176426130 1.712398e+00 -0.1736599798 0.5006684625
## [574,] 0.3288808593 0.141403002 2.354183e+00 0.2932977004 0.3234055028
## [575,] 2.7862372119 0.312850715 1.810713e+00 2.1676335163 0.2724163872
## [576,] 0.2882093222 1.397338952 9.135795e-01 0.2580596124 1.6207237157
## [577,] 1.7811187547 0.938338573 9.167921e-01 1.0997342100 0.4337611066
## [578,] 1.3214237882 0.119496625 -9.110244e-03 -0.1139284172 2.4822498840
## [579,] 0.7330639543 0.481551569 1.261396e+00 0.5156278733 -0.0738510565
## [580,] 2.2735325260 1.114605883 5.416031e-02 11.6132244576 -0.2008199207
## [581,] 6.9543103888 1.073968451 2.200294e+00 -0.1257036083 0.0618144722
## [582,] 1.1822974254 0.247283182 9.924390e-01 1.0918040960 0.7935740599
## [583,] -0.0995693938 0.768629927 5.493510e-01 3.0632470237 0.7329212994
## [584,] -0.0217942871 0.084853876 5.303474e-01 0.7224801219 0.5250698776
## [585,] 0.8725508275 0.179472525 9.466033e-01 0.5532721385 0.0738200812
## [586,] 4.0221536590 1.023833568 -3.055729e-02 0.5947885167 1.2298316492
## [587,] 0.9668673570 -0.117228694 3.405565e-01 1.2503360284 0.7146109706
## [588,] 0.6905890660 0.413152338 6.470662e-01 0.9441649920 0.0669207310
## [589,] 1.2745805775 0.152006113 1.081614e+00 1.3544930591 2.5631405861
## [590,] -0.5015643218 2.472479178 1.536530e+00 1.0681755156 1.0034798763
## [591,] 0.9380056456 0.693262018 6.129571e-01 0.9577113655 1.2734490110
## [592,] 1.9335827306 3.961948692 4.805593e-01 0.2605392182 1.2893489240
## [593,] 1.4985227768 0.452609211 2.218672e-01 0.2880302257 -0.0444072588
## [594,] 0.1817911080 0.361277127 6.053233e-01 -0.2670161876 0.3514499877
## [595,] 0.8223674313 3.025113129 4.317650e+00 0.7888815544 0.3923860291
## [596,] 2.0755237610 2.217903211 1.430388e-01 0.3790790125 0.4281127983
## [597,] 0.1949173356 0.925557960 2.500622e+00 0.5025394413 0.2116965614
## [598,] 1.0477513582 -0.014197322 6.547664e-01 0.7247652204 3.0023517988
## [599,] 6.5756085221 0.736911276 1.737301e+00 2.8003181352 1.1409602764
## [600,] 1.3361899357 0.018876440 2.211694e+00 0.1470801179 -0.0993684775
## [601,] 0.5521897213 0.426699907 1.075645e+00 1.2691664382 4.5805772580
## [602,] -0.1803385240 0.617236103 1.103897e+00 0.1064958984 3.1364410528
## [603,] 2.7484116902 3.064650717 2.588589e-01 0.0812938277 0.9230971740
## [604,] 2.6113480545 1.753238431 2.097370e+00 0.2728909644 2.7107949689
## [605,] 0.7914405410 -0.851148640 2.206574e+00 0.4225574077 0.6188576564
## [606,] 2.4518901539 0.494187461 1.048663e+00 0.4631983656 -0.3141519259
## [607,] 0.5283426925 1.244473400 5.020827e+00 0.3400537295 1.8100521687
## [608,] 0.8836925270 2.194396016 5.014609e-01 1.1060132609 0.8054108792
## [609,] 0.0786514624 0.563088788 -8.088954e-02 0.6892317017 1.7245503840
## [610,] 0.7303374083 0.321149398 3.064456e-01 0.5338752739 0.6604469980
## [611,] -0.1266829108 0.048454923 7.130547e-01 0.0672718931 0.5715107029
## [612,] 0.9795111676 3.252732207 2.244101e+00 0.7302787195 2.7804912430
## [613,] 0.2836250138 1.615359963 1.196914e+00 -0.0366099341 0.5656999793
## [614,] 0.3211792279 0.591741005 1.377122e+00 0.1216806326 0.6141909971
## [615,] 2.0723017987 0.452928837 4.542330e-01 0.0317172278 0.5108963960
## [616,] 0.9650584101 1.271661768 7.224865e-01 0.3824913564 0.3369301991
## [617,] 0.2119132871 0.797918764 8.214919e-01 0.2016490346 1.3649083178
## [618,] 0.9738681009 0.843713444 3.272805e-01 1.5901811129 0.7322594979
## [619,] 0.0656665827 5.826313806 3.642518e-01 0.0745208590 1.1318465833
## [620,] 0.3767127072 0.696376213 -1.148830e-01 0.5224184654 0.3006037438
## [621,] -0.2080171227 0.341779677 4.840447e-01 0.3320902923 1.2474792729
## [622,] 3.0232481920 0.654073020 4.770510e-01 0.3427906351 0.1024302901
## [623,] 1.5160852952 0.446616689 1.616758e-01 1.1353427862 0.6833775654
## [624,] -0.1693207855 0.277590075 1.288912e+00 1.2784365397 3.8741834010
## [625,] -0.1223129649 0.240456629 8.433654e-01 0.8390127166 1.2469825023
## [626,] 0.3033092412 1.390186596 8.608326e-01 0.3705490367 1.2166582359
## [627,] 0.3099539066 0.293626808 4.112915e-01 1.3698738312 0.1139278997
## [628,] 0.5694280476 0.785506102 8.828401e-01 2.1444570569 3.6659070767
## [629,] 3.5148344505 0.615238275 1.065443e+00 -0.1315251947 -0.0018879042
## [630,] -0.0807041211 0.076331720 3.231489e-01 2.8198962415 0.1571959081
## [631,] 0.7421969096 1.908468492 2.896090e-01 1.4281092735 0.4279360973
## [632,] 0.1472731704 0.862631202 3.077910e-02 0.2451151973 0.3757233195
## [633,] 1.1879396948 -0.022193225 5.257435e-01 2.5061969967 0.0119201896
## [634,] 1.9375271895 0.244381830 5.618935e-01 1.1333777918 0.4124310617
## [635,] 0.2461527108 0.669531712 9.075883e-02 3.8143401737 4.9934150383
## [636,] 0.3164827756 2.001431646 8.149385e-01 4.5472164261 -0.1001116819
## [637,] 0.0698877591 2.177154996 1.232937e+00 0.2145965729 2.4795721821
## [638,] 1.5582781543 0.068261572 1.418490e+00 3.4289377658 1.1357988420
## [639,] 0.2696137748 0.429814391 5.357804e-01 0.5862344247 1.7555273953
## [640,] 0.3238010475 0.880981385 1.537849e+00 -0.0438304659 0.4477812274
## [641,] 1.1482669581 0.795256543 4.102018e+00 2.3361171690 0.6265328122
## [642,] 0.2669708277 0.529916057 6.615550e-01 2.6415239336 0.8833301027
## [643,] 1.5801111484 -0.449923377 4.922790e-01 1.5330791921 0.3758120441
## [644,] 0.6578960734 2.386864190 9.550618e-01 1.2641927681 1.2169183772
## [645,] 0.3122271386 1.211930423 5.698900e-01 0.4708644536 0.4674969646
## [646,] 4.6510949790 0.349796094 3.956467e-01 3.0319636380 1.5646766085
## [647,] 0.3026496877 0.644915388 9.073386e-02 0.6775186569 2.5438757591
## [648,] 1.4453220495 0.441425127 1.434246e-01 0.2813934087 0.4209399448
## [649,] 2.0898164423 0.946553861 4.750087e-01 1.4461077553 0.5559104340
## [650,] 0.0945049875 -0.051662329 1.422650e+00 0.3733153809 -0.2380739126
## [651,] -0.2315583682 0.379568945 4.287220e-01 0.7877528782 1.3986232790
## [652,] 0.1123548256 0.256250446 6.331366e-02 -0.1508431575 0.6298605148
## [653,] 0.2197376604 0.274187289 9.050252e-01 0.7497414043 0.1442345963
## [654,] 0.1949495021 0.168366696 5.357660e-01 1.7397348770 0.2050227408
## [655,] 0.5639746177 2.646274844 7.241420e-01 0.6361842032 0.4573661679
## [656,] 4.9894599990 0.141403699 2.728456e-01 2.6444775933 0.2092655675
## [657,] 1.1248262687 0.312217342 4.078375e-01 0.5028244044 0.0425120300
## [658,] 0.8063642288 0.400151036 8.467523e-01 0.2769979685 0.6224838825
## [659,] 0.0868929817 0.024356074 8.331473e-01 -0.0549563435 2.6970501340
## [660,] 1.1551372909 0.111460935 1.701826e+00 0.3526547941 0.8091404543
## [661,] 0.8035113843 2.567304869 3.294710e+00 2.8151543531 0.3175852303
## [662,] 3.1459483472 1.430103228 3.347077e+00 1.1147905201 0.3063296098
## [663,] -0.0315869521 0.740562338 2.204910e-01 0.8841618755 2.5388277009
## [664,] 0.3099381123 -0.191361611 1.245901e+00 1.4673605449 -0.0369480586
## [665,] 1.8539064305 2.725197088 9.843276e-01 0.1794957789 0.5688472685
## [666,] 1.9749445533 -0.072597269 3.212322e-01 0.3764193337 0.2489112801
## [667,] 0.0364226109 1.354512095 1.960174e+00 0.2951877469 0.3318122674
## [668,] 1.1842841537 3.927150213 2.934080e-01 -0.0079463151 0.5596829763
## [669,] -0.1383537934 1.421514687 -2.703783e-01 2.5854191476 0.8496540772
## [670,] 0.2658294556 1.233878011 6.088472e-01 0.9964648984 -0.0495307726
## [671,] 0.5242143872 0.504935997 5.115033e-01 1.9494696115 0.6303304627
## [672,] 0.0493158390 0.082474257 1.149429e+00 4.0353758419 0.5153419481
## [673,] 1.0980880292 0.273774175 3.239349e-01 0.5027506645 1.8573781233
## [674,] 0.9965784241 1.104465956 8.739790e-02 0.2612924959 1.2639490665
## [675,] 0.4205999266 0.109863630 1.030034e+00 0.7276132395 -0.1264762656
## [676,] -0.0062424055 -0.023577446 1.251424e+00 0.4420620844 0.5786351338
## [677,] 0.3732512287 1.559645033 2.270552e+00 2.7285435489 0.8534957594
## [678,] 0.5554603266 0.494684482 -1.068319e-01 -0.1889322332 1.7862492849
## [679,] -0.2181630505 0.904977150 1.218973e+00 0.4501230048 0.0092573068
## [680,] 7.3842376920 0.380309777 4.244098e-01 1.0997083404 3.0835933462
## [681,] 1.8769276442 1.499208063 1.334103e+00 0.3869269217 0.6344704187
## [682,] 2.0202364976 1.166274046 -9.013878e-02 1.8485432251 0.1679766359
## [683,] 2.2328529583 3.349478889 9.827101e-01 0.8246917670 1.3520637343
## [684,] 0.0524130081 1.009002248 1.410952e+00 0.6231641013 1.0039872497
## [685,] 0.4745267293 1.169754104 9.962609e-01 2.3561819132 1.2511705748
## [686,] 4.8193661467 0.484681057 2.201669e-01 0.1886663092 0.2718147805
## [687,] 2.4893847617 1.375514180 1.857171e+00 3.3510357402 3.2076317401
## [688,] 0.5440982387 0.827939259 2.675387e+00 0.7547340369 0.6072476638
## [689,] 0.9422639809 0.662646343 9.440702e-01 -0.0083521787 0.1390018104
## [690,] 2.3588639944 0.165130386 2.532477e-01 1.4892747977 1.0448231596
## [691,] 0.0320332219 1.015597894 2.403641e-01 2.3769132981 0.3325201499
## [692,] 1.0008873093 1.488777174 2.761213e+00 0.5699681367 0.9016184855
## [693,] 0.3996239919 0.679411612 9.293670e-01 0.0420439751 1.8957992726
## [694,] 1.2368271883 0.381168537 8.611761e-02 1.1846829316 0.4829221446
## [695,] 0.8437255114 0.167693400 -5.681162e-02 -0.1327449777 0.6703991686
## [696,] 0.3370227489 0.933535233 3.877256e-01 0.1957486755 0.5179872057
## [697,] 3.8771664263 3.934041709 1.126895e+00 2.5772494947 0.7084376368
## [698,] 0.4061617654 0.880067319 3.330120e-01 -0.0995957014 0.6585191535
## [699,] 0.5794839508 -0.132063961 3.384368e-01 1.4071721517 2.0119031739
## [700,] 0.4234482341 1.419446675 1.744772e-01 -0.0828314140 0.3818511346
## [701,] 6.7905562261 1.500232695 -5.908741e-01 1.6019191221 0.8105566082
## [702,] 1.9880933532 0.578722942 1.196684e+00 0.5688363553 0.3675746400
## [703,] 1.5606559624 0.574795314 -3.114563e-01 0.6337513851 3.7739285798
## [704,] 0.5142572762 1.440487723 1.753638e-01 0.6102261070 1.1433834406
## [705,] -0.3296007401 2.385072091 2.714653e-01 1.1363507610 0.8729160918
## [706,] 1.5007500915 0.161517694 1.273808e+00 0.0915366349 -0.0392132654
## [707,] 0.6077813057 0.465650515 1.251514e+00 0.8394710255 -0.0693866047
## [708,] 1.6066760160 1.262100442 4.107195e+00 4.1680278896 1.2516736509
## [709,] 0.9822841697 2.088407681 1.201076e+00 0.1791085737 0.2493967171
## [710,] 0.8967187431 -0.053650930 1.161577e+00 0.5207366830 2.2777853405
## [711,] 1.8183333557 0.251126967 1.130008e-01 0.8789662049 1.7631006041
## [712,] 3.4715379940 2.454765132 2.245935e-01 1.9487088716 3.0082584407
## [713,] -0.2184092960 0.251365261 6.432796e-01 0.7637530347 -0.1382430688
## [714,] 4.5518181215 3.214768197 3.089892e-01 0.2214139190 0.3227292054
## [715,] 1.3604959308 0.868143827 -4.383924e-02 0.7706091305 0.1568678607
## [716,] 0.9951881815 0.538049410 -3.874982e-02 0.4501685249 0.9132659691
## [717,] 0.4881674388 0.560023251 1.068633e+00 0.4769688938 -0.1773882838
## [718,] 0.4612109944 0.061511152 9.601232e-01 -0.0357571341 0.5349325919
## [719,] 2.1298536647 0.408388008 1.298528e+00 0.3470939361 0.6846525796
## [720,] 0.3081857105 0.130163359 8.022831e-01 0.2392396533 0.4309012147
## [721,] 0.0442449006 0.961133243 9.119500e-02 1.0951732537 -0.1143841926
## [722,] 0.8623450306 0.535393318 -2.384452e-01 0.4267150977 0.6672021366
## [723,] 0.7196675157 1.820860607 2.332848e+00 0.3780937797 1.0292716126
## [724,] 2.1772762512 1.166953234 1.154389e+00 1.3057139057 0.5954818763
## [725,] 1.5955794687 0.774310152 5.589462e+00 1.2982923891 0.2400801927
## [726,] 2.0828840477 0.342009366 1.531083e-01 0.6428882133 0.3476486965
## [727,] 1.7269921151 0.118481212 4.244007e-01 -0.0434339613 2.8450498795
## [728,] 0.5309984925 3.537230879 4.613444e-01 0.9450950592 0.6693861909
## [729,] 0.2324258312 0.608779723 6.209616e-01 0.0479086983 2.6645168067
## [730,] 0.3781837359 1.353337448 7.197105e-01 1.5588295464 0.1292656212
## [731,] 0.2145428569 5.498603912 3.006271e+00 1.1176919033 4.4657789801
## [732,] 1.0404005488 0.020609510 7.544958e-01 0.7754802397 0.1237407700
## [733,] 1.7419182030 2.323256698 5.332600e+00 1.0349423603 0.1871011709
## [734,] 0.0270423695 1.153292730 1.499745e+00 0.9068721710 1.7431039730
## [735,] 0.4375843048 5.422781323 3.997163e-01 0.4019452603 0.6278688744
## [736,] 0.8695576330 0.337266894 -1.726974e-01 0.0218447934 0.3387946604
## [737,] 0.9659837741 1.466938661 1.211933e-01 2.3469721991 0.6425170314
## [738,] 0.2185832815 0.718845572 8.158084e-01 0.2609476264 0.2676156718
## [739,] 2.4564325462 0.104003557 2.219578e+00 0.9519330562 1.6414028296
## [740,] 1.2608068361 1.489716808 4.041456e-02 0.6204392135 2.0634704423
## [741,] 3.9440265380 -0.114012109 3.874852e-01 2.4652855824 2.1063486368
## [742,] 0.0256078080 -0.048196255 -1.144630e-01 0.6926302462 0.9713496557
## [743,] 3.2143603409 0.257345114 1.854817e-01 0.0206848399 1.5489619987
## [744,] 0.4600600432 2.123101604 6.919245e-01 0.7369769471 1.4084441386
## [745,] 0.4034329111 0.123824474 8.175372e-02 0.4026990943 1.9825696908
## [746,] 1.4777188257 1.567628352 7.689323e-01 0.3012433031 0.2083317506
## [747,] 1.7962480565 1.361089760 -2.567174e-01 -0.1369790292 0.5993222586
## [748,] 0.6611879994 2.258890395 1.203685e+00 -0.3546135975 1.2520855883
## [749,] 1.8760286680 4.503209779 2.217099e-02 2.3304150066 0.9725382986
## [750,] 2.1999411478 1.236922798 2.719126e-01 2.3832191530 5.7621622065
## [751,] 5.7516168355 0.781538530 1.303712e+00 0.3490125443 2.0634113689
## [752,] 7.3946586974 1.184512106 1.627298e+00 0.1482443741 1.4375150656
## [753,] -0.0750098824 2.489255847 6.359416e-01 0.3566972572 0.8248949574
## [754,] -0.2959975944 0.997116883 1.402154e+00 0.2012945718 0.5550950665
## [755,] 2.0962151975 1.737139010 2.143127e-01 3.4135990166 0.0571824107
## [756,] 1.5036133693 2.665640600 4.935654e-02 2.3605077439 0.2477467369
## [757,] 6.0438449411 0.767153876 5.315742e-02 1.6903341736 0.5514014808
## [758,] 0.3134513604 -0.055246325 1.853787e+00 2.1013151529 1.6979017283
## [759,] 1.1243732913 0.175843539 5.235026e-01 0.6725642656 0.2037299350
## [760,] 1.4891080335 -0.224155423 2.237782e+00 0.0785505386 0.6107074970
## [761,] 0.5395263692 1.245129262 1.573634e+00 0.0160065340 0.8262293854
## [762,] 0.0393669894 0.021976567 7.022551e-01 2.4332699409 0.7685732535
## [763,] -0.2483020793 0.490087449 1.289765e+00 2.9613328615 2.2074813791
## [764,] 1.5460556991 0.077457864 7.692190e-02 -0.1565764391 0.0637769372
## [765,] 1.7281292898 1.377181453 2.861575e+00 0.1209639474 0.3342713214
## [766,] 0.5123065216 1.550367414 1.953579e+00 2.0424212478 1.0820098642
## [767,] 1.0958581869 6.503705022 9.153764e-01 0.4156455046 -0.0823728284
## [768,] 0.1917215856 1.275905155 2.869049e+00 2.2540402687 0.5029177965
## [769,] 0.5978157097 2.160265809 -1.422626e-01 0.4127047158 0.0746618426
## [770,] 0.8840217722 1.065847852 2.180852e+00 0.6373841447 4.2592532456
## [771,] -0.5149934893 1.086389906 2.621041e-01 -0.0180998909 0.5082829294
## [772,] 1.3293557037 2.278200381 5.632012e-01 0.9241232217 1.2453936347
## [773,] 1.0893014381 -0.157293116 1.128013e+00 1.8662987907 1.5939257834
## [774,] 0.4315369232 0.748262779 4.018974e-01 0.9345380761 2.3414243195
## [775,] 0.8739085249 1.332990896 -7.584166e-02 2.6133676270 0.5404548009
## [776,] 2.3780113011 2.543960858 4.918461e+00 -0.0276526838 -0.2375890614
## [777,] -0.1182604960 0.177460630 9.041606e-01 0.3261234030 4.2694656871
## [778,] 0.2633737402 1.024197215 6.305448e-01 1.9998079605 0.4122005381
## [779,] 0.7516554727 0.103642202 1.112532e+00 1.3450552334 0.2720778409
## [780,] 0.1360403324 0.475588776 8.349757e-01 0.9270331815 4.5106340048
## [781,] 0.7033588448 -0.009941351 7.836319e-02 1.4488003998 0.0405258198
## [782,] 3.9800170615 1.651980869 3.281175e+00 2.2778690696 0.4736737153
## [783,] 1.5524046916 0.282105453 2.951766e+00 1.0263895280 -0.0336059063
## [784,] 0.4653154879 0.304038969 1.008249e+00 0.7441964746 0.1442600846
## [785,] 0.2491716814 -0.039288260 1.093209e-01 0.1895570057 4.7746066491
## [786,] 1.7974840979 0.530623984 8.861136e-01 3.2239124371 1.3110943838
## [787,] -0.0262464255 -0.200491312 1.322347e+00 3.4269979859 1.3632804390
## [788,] 0.9023033339 3.385862130 1.400402e+00 0.0492431174 1.1157490408
## [789,] -0.0619067002 1.669458033 1.886161e+00 0.8933842840 1.3585892770
## [790,] -0.0208839294 0.497231683 1.021524e+00 1.7465598001 0.2515498190
## [791,] -0.1527843010 0.106200397 5.127281e-01 -0.2128066943 1.3048817029
## [792,] 0.7454038299 0.228026871 1.076935e+00 1.0455800203 2.0862216656
## [793,] 0.3165580711 0.868501717 1.015378e+00 0.5399965685 0.5623683045
## [794,] -0.1488649489 1.219206642 2.187337e+00 0.0748805417 0.7248106143
## [795,] 0.3419212756 0.708148719 4.407002e-01 0.3282627754 0.1273439619
## [796,] -0.2251809256 1.194087902 1.708203e-01 0.0865650271 0.8241656738
## [797,] -0.0827026993 3.490707174 1.110828e+00 0.7362437133 0.5778219791
## [798,] 0.0535285445 0.170854419 1.750021e+00 1.0615225890 0.5664144308
## [799,] 1.6090868716 0.703243595 3.761153e-01 1.2175225496 1.7703711732
## [800,] -0.1472542249 0.206563199 3.599644e-01 4.4908320342 0.2325706483
## [801,] -0.0339372691 0.838168867 1.552474e+00 0.5592788294 3.0941602878
## [802,] 2.4655504979 1.075871062 2.894049e+00 0.3048863230 0.2066671663
## [803,] 0.1638948491 0.049706298 1.529066e+00 0.3705782892 0.8721576165
## [804,] 0.1641026759 1.959142336 3.202119e-01 0.4075282816 -0.1586364806
## [805,] -0.2583265582 0.606513668 9.449067e-01 0.6108337015 0.0886485530
## [806,] -0.1489005073 0.292319378 1.210655e+00 0.3918443743 1.8419572472
## [807,] 0.5037549986 1.195349010 2.629999e+00 1.4166590663 1.4599899101
## [808,] -0.1094207325 2.843189040 1.391976e+00 1.4490507561 1.5192475314
## [809,] 0.6660933566 2.109834916 -1.570324e-01 0.5425842575 -0.1915178800
## [810,] 0.9291342244 0.999608933 9.324936e-01 0.6930087673 0.0990060442
## [811,] 1.6844106387 0.326866343 4.971207e-01 1.6554683535 1.3614004590
## [812,] 0.2242371091 -0.187660416 1.843398e-02 3.0550322506 1.3738895818
## [813,] 0.6639471228 1.206681158 1.827375e+00 1.2987221762 2.4006350319
## [814,] 0.1251979152 0.565651718 -8.336723e-02 1.4409942262 2.3061614848
## [815,] 0.6196903239 0.283241629 2.537114e+00 0.8527122789 1.3277311974
## [816,] 1.9111685645 0.598949871 1.025863e+00 0.8923823397 0.4597034524
## [817,] 0.4771870209 0.392071352 6.009725e-01 -0.2105936225 0.1473178146
## [818,] 1.1992197756 1.648888435 1.291501e+00 -0.1898362925 2.3072544287
## [819,] 0.0335085948 1.646221291 7.010977e-01 -0.0889597126 1.0198939222
## [820,] 0.0496444177 -0.035285656 7.775296e-01 0.2119008087 1.9547092360
## [821,] 1.9717746083 1.331924592 1.023362e+00 -0.1246776813 1.9047113593
## [822,] -0.4760603460 2.808742628 5.046314e+00 0.0566304277 0.1406674464
## [823,] 1.5090708158 0.377154529 1.106343e+00 0.5417166892 0.4087407707
## [824,] 0.2243694539 0.128784362 1.473088e+00 1.8223779363 0.6866048337
## [825,] 0.4524131301 7.536258409 -2.022883e-01 0.7791905444 1.3094833759
## [826,] 0.4943096992 1.656286950 1.314553e+00 1.6858923860 0.3425650325
## [827,] 1.1532957418 0.482741133 4.219855e-01 0.3556483539 -0.0260736722
## [828,] 1.5853441588 1.257511109 5.717487e-01 1.0862881784 0.2207953246
## [829,] 0.0270711571 0.435158693 4.178400e+00 0.0483528166 1.5898604817
## [830,] 1.2782449882 0.072185971 -1.929751e-01 2.0784183064 0.1586121049
## [831,] 0.6940079121 0.173713117 1.192704e+00 3.3956811583 0.3238939695
## [832,] 2.7965983708 0.638418757 4.417502e-01 3.5308261050 1.0243109000
## [833,] 0.3886862701 0.961621742 6.707471e-01 0.9473621951 0.6751906285
## [834,] 1.3791463711 1.373993229 -1.810638e-01 3.2864264668 -0.1779023760
## [835,] 1.3235595296 1.060359494 2.119342e+00 0.8619899338 0.9457199328
## [836,] 0.9412765553 -0.260345717 8.752948e-01 0.1256115871 1.9232522678
## [837,] 1.0093306763 0.543009193 4.704374e-03 -0.1914116014 0.1270322057
## [838,] 0.3591716595 0.358516111 3.524686e-01 0.2386030654 1.0966549863
## [839,] 1.0983515702 0.858221329 3.296026e+00 0.0906215361 0.7253029703
## [840,] 0.7936722704 0.167921029 -5.402520e-02 3.9980033565 0.6658786302
## [841,] 0.7988601383 1.755486612 -1.490824e-01 0.9520184947 0.1799451489
## [842,] 0.0503220786 0.866535614 1.917580e+00 1.0201640198 0.3012180357
## [843,] 1.0523974634 0.655078792 1.419641e-01 1.1896746734 -0.1524113767
## [844,] 1.3576949607 1.394263997 7.232841e-01 4.1284799478 4.0718873805
## [845,] 2.8849234577 2.167700161 -5.614885e-02 0.4129319665 2.5379111666
## [846,] 0.6967236150 3.773082373 8.066238e-02 0.5129701030 1.0367501976
## [847,] -0.1729524920 0.334886818 1.099871e+00 0.3874262462 0.3707741070
## [848,] 2.1735349224 0.497140987 5.313483e-01 0.5787750340 2.4363499908
## [849,] 2.8569127351 2.361136505 7.937957e-01 0.0393453855 0.1457752027
## [850,] -0.2813435551 1.660104079 3.612266e-03 0.8808754668 2.0866721382
## [851,] 0.7994737881 0.444262810 4.341465e-01 3.1008841924 0.2426866960
## [852,] 0.1796608077 0.458511940 -2.265067e-02 1.3310152723 3.5235801219
## [853,] 0.8865466751 0.300813732 3.869064e-01 0.7475534477 -0.3002543485
## [854,] 2.0764715877 0.141421277 5.411097e-02 0.2198660613 1.9345385655
## [855,] 3.0526788929 -0.197014481 2.264037e+00 1.6322320863 1.9686105907
## [856,] 1.6771295715 0.643329383 4.095912e-01 1.1004026265 0.0291214929
## [857,] -0.0763819275 0.443454494 2.524862e-01 0.8543295821 0.4279884054
## [858,] 1.0312303503 0.476129900 1.822466e+00 0.8520577191 1.4057825657
## [859,] 1.1642921189 2.716322619 1.117965e+00 1.6938418305 0.7646108397
## [860,] -0.0352443054 7.116186436 1.925496e+00 -0.1774670046 1.3837258032
## [861,] 1.3971798876 0.305824529 1.146241e+00 0.4941888880 0.4575597195
## [862,] 1.1329543153 0.211017688 2.527727e+00 0.4757581991 3.0222484206
## [863,] 0.3308073278 0.083409946 3.923267e-01 0.5145492998 2.2556463778
## [864,] 0.3615995673 6.542241862 7.947186e-01 0.6950261612 2.5054415914
## [865,] 0.0073474185 0.869958045 9.684161e-01 0.2053622347 0.5319804304
## [866,] 1.0616863745 0.514057537 7.498886e-01 0.9807011869 1.0984233243
## [867,] 0.1010006801 1.455146670 1.122933e+00 -0.0793331859 0.1324376462
## [868,] 3.8129945698 0.538080897 7.842982e-01 2.0740304793 1.1569539692
## [869,] 0.2243194423 0.846017093 2.643721e-01 0.0844665217 0.7176017116
## [870,] 2.3773832819 1.271889519 6.997007e-01 0.3646226379 0.8512654372
## [871,] 1.9962278643 -0.087275966 1.974334e-01 0.5199761374 0.4795919568
## [872,] 0.2453311818 0.979697464 9.399251e-01 2.2690589433 0.1265644507
## [873,] 1.0331912121 0.166299659 6.767001e-01 3.3657351072 -0.1876732352
## [874,] 0.3077421543 0.255388182 1.821048e-01 0.2126963066 1.4593981083
## [875,] 1.7733833737 -0.059674736 1.293780e+00 0.9369745370 -0.3804122365
## [876,] 0.4275032460 0.439542760 1.816041e-01 1.4981954316 0.8624895722
## [877,] 2.2161436273 -0.017938833 9.208492e-01 0.9614514645 -0.0302622886
## [878,] 1.9341594505 0.244292675 -1.374327e-01 1.1569511854 0.8527016691
## [879,] 0.2000480880 1.410385573 -1.071755e-01 1.1786841059 0.3859058958
## [880,] 1.0920972795 0.786509221 8.438539e+00 1.4603672720 -0.1039914243
## [881,] 1.5066925375 0.311347179 2.114293e+00 0.5750921226 0.1498532432
## [882,] 0.3252647224 0.757760736 6.273208e-02 0.9544991980 0.2886648299
## [883,] 0.7302227087 1.106959434 5.750672e-01 3.6766619740 -0.0512853377
## [884,] 3.2096282580 0.076864730 1.291579e+00 1.1201382408 2.0345189715
## [885,] 0.5054938808 0.333466038 7.812225e+00 0.6789900294 2.8396039644
## [886,] 0.1441266946 1.437272490 -1.615670e-01 0.3390021479 2.7079400596
## [887,] 0.0031473098 -0.205267961 1.431411e+00 0.8255894023 -0.0341747177
## [888,] 0.5712809055 0.039263472 6.420094e-01 0.0173580572 0.8969654602
## [889,] 1.2223167180 -0.362941676 2.210229e-01 0.0649746222 1.6458598250
## [890,] 3.3383907569 0.863189111 2.497442e-01 -0.0800072238 5.7210636463
## [891,] 1.1135372500 0.085348708 -1.251066e-02 1.9159195809 1.7380460355
## [892,] 0.5010137345 0.556480456 1.560175e+00 -0.2345528434 0.1169865308
## [893,] 0.6075874591 1.457554668 1.528334e+00 0.3863511110 0.8901862380
## [894,] 1.0853306172 1.371499984 7.648758e-01 -0.0637235513 0.1327613567
## [895,] 0.0098921361 0.356436824 3.163353e-01 1.5655217063 -0.2325822879
## [896,] 0.8874588410 3.095341372 5.269681e-01 -0.1331212263 0.5237143486
## [897,] 0.9295798766 -0.246056287 2.100730e+00 2.1771581483 1.2380889713
## [898,] 0.3031690256 9.319403588 4.326620e+00 1.1446779739 0.8392307490
## [899,] 1.2865960911 0.050744538 3.332531e-01 0.1752857757 2.0955921427
## [900,] 0.6430278566 0.378672396 9.140850e-02 0.5630544601 0.3117430637
## [901,] 4.3729108726 0.081818097 4.143416e-01 0.2040962196 2.4230073214
## [902,] 0.0864407229 0.106940610 6.360843e-01 0.6409489120 0.5638411247
## [903,] 0.2952174863 0.399444155 1.541372e+00 2.3926241319 0.0317853197
## [904,] 0.5994001759 0.624538022 3.486266e-02 2.4885116357 0.7121435690
## [905,] -0.0056538006 -0.192055619 -2.102022e-01 0.5677587674 0.3615287690
## [906,] 0.5752436147 0.108982189 2.521474e+00 0.7971448408 3.0863365120
## [907,] 0.2167961214 0.242440409 3.976376e+00 0.6463680313 0.8677091237
## [908,] 0.3053690670 0.594727416 4.122376e-01 0.6016001895 0.2763735224
## [909,] 1.1290236759 0.223990640 1.422157e+00 -0.3551714737 0.0429970476
## [910,] 1.6817022846 1.709078824 1.970924e-01 0.1880325928 0.9133363740
## [911,] 0.4712778155 0.988636332 3.864188e+00 1.2199639465 0.7264191288
## [912,] 1.8581050182 0.120484847 3.942363e-01 1.1626051210 2.9630050876
## [913,] 0.2145923133 0.859394839 1.982153e-01 0.9916162682 0.3031341068
## [914,] 0.6171789760 0.137239538 3.585715e-01 1.2624791087 -0.0353819825
## [915,] -0.0264747373 0.114165080 9.201949e-01 -0.0330592431 0.2669859315
## [916,] -0.4635092641 2.241988293 2.485760e+00 0.4012448708 -0.0834811724
## [917,] 5.0923093827 1.869565907 8.031053e-01 4.5244204063 0.6920689861
## [918,] 1.4554254979 0.738150562 1.427177e+00 -0.1659761265 0.8279491993
## [919,] 0.3215176941 1.123209775 1.917987e-01 0.5875041036 0.2408042548
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## [671,] 0.1744904747 1.704464526
## [672,] 0.7165050687 -0.060021408
## [673,] 0.9571043164 1.985449064
## [674,] 3.1283603033 0.821774297
## [675,] 0.4311267210 0.850392731
## [676,] 0.7223947193 0.297107803
## [677,] 0.4263171734 0.558342620
## [678,] 0.2358401766 1.136955145
## [679,] 0.5870297044 0.014114166
## [680,] 1.6318780694 0.842982212
## [681,] 0.3655824696 3.313878725
## [682,] 0.4579053998 1.150125611
## [683,] 0.4576788670 0.051889617
## [684,] -0.0896991359 -0.214872236
## [685,] 2.2067742029 -0.213606003
## [686,] -0.2639072602 0.843139617
## [687,] 1.6294639780 1.055028312
## [688,] 1.1427114065 2.547759060
## [689,] 0.0004817714 0.854118754
## [690,] -0.1295825101 -0.271798521
## [691,] 2.2599545224 0.142661955
## [692,] 0.8686477841 0.694865029
## [693,] 0.6549271675 1.402882254
## [694,] 0.3360296861 2.557839456
## [695,] 0.4069642406 0.557009593
## [696,] 0.1212958998 0.313795365
## [697,] 0.0263668594 2.520195623
## [698,] 6.8577443144 0.474320526
## [699,] 1.1617542576 0.459417032
## [700,] -0.1360462978 0.758310382
## [701,] 2.5771628099 2.674685132
## [702,] 0.3211035889 -0.070501530
## [703,] 3.1257072550 -0.051703914
## [704,] 0.3407657833 1.279381887
## [705,] 1.9254144100 0.657836918
## [706,] 0.7791203772 0.790260895
## [707,] 0.1683476247 0.670626112
## [708,] -0.1084818477 0.412257036
## [709,] 1.9440278293 2.366261236
## [710,] 2.6665114486 0.340892670
## [711,] -0.1712149691 0.638550652
## [712,] 0.4357429537 1.176932174
## [713,] 1.1577362191 0.317755836
## [714,] 0.4665706407 0.472472613
## [715,] 0.2739500748 1.701776680
## [716,] 2.1558939634 0.821923743
## [717,] 0.9864417016 0.588450817
## [718,] -0.1548114038 0.978500856
## [719,] 0.4338173904 8.139010844
## [720,] 0.5845734277 0.151823914
## [721,] 0.1066787577 0.463123077
## [722,] 0.7359606455 0.713849807
## [723,] 0.7931594039 0.020506267
## [724,] 2.0826191826 0.196270856
## [725,] 0.5390778237 1.496859595
## [726,] 2.1587396627 0.522846208
## [727,] 1.3372121741 0.234592515
## [728,] 1.4504242879 1.206869508
## [729,] 0.0883777856 -0.077940212
## [730,] 1.4818751486 0.423612692
## [731,] 1.1093294807 3.753922031
## [732,] 0.1004706764 1.370347095
## [733,] 1.0726967225 0.475501935
## [734,] -0.0159355557 3.067301037
## [735,] 0.2807731734 -0.036147534
## [736,] -0.1745057029 2.559752606
## [737,] 3.3282763668 -0.060865010
## [738,] 0.0786294554 0.246082423
## [739,] 0.2076049914 1.380073291
## [740,] 0.4453255752 1.151374787
## [741,] 1.2652472450 1.956447335
## [742,] 2.8865205920 0.963656297
## [743,] 0.3563837556 6.418380918
## [744,] 1.1890241324 2.302888469
## [745,] 0.0022513960 0.412681371
## [746,] 0.3946369088 -0.252737462
## [747,] 1.6978580641 0.790183779
## [748,] 0.1085038493 0.067689131
## [749,] 2.4774546063 0.877184640
## [750,] 1.0194455535 0.443650015
## [751,] 5.1512229559 3.976600377
## [752,] 0.1255685319 1.463177991
## [753,] 3.5526122096 0.537520055
## [754,] 0.3415923213 0.540558771
## [755,] 0.3240452393 1.735772637
## [756,] 2.9291042519 0.101871125
## [757,] 4.9008311557 3.045936070
## [758,] 0.0542486228 0.041408707
## [759,] 0.4900724740 2.129452334
## [760,] -0.0182745014 0.690029991
## [761,] 0.3569421866 0.275996534
## [762,] 0.9966103648 0.597219363
## [763,] 3.0251112797 1.691393828
## [764,] 0.4849942400 0.078261491
## [765,] 0.2677481520 1.462043794
## [766,] -0.4030587934 1.729363981
## [767,] 2.6794201252 0.591882178
## [768,] 0.2836822974 0.853636993
## [769,] 1.9701397698 1.327300457
## [770,] 2.4394661816 0.472750028
## [771,] 1.2046099342 0.509288911
## [772,] 2.7947392521 -0.109486036
## [773,] 2.7759363726 -0.293105143
## [774,] -0.0357302120 0.096556271
## [775,] -0.1018734314 1.031827149
## [776,] 1.4598730227 3.132120604
## [777,] 2.6388399615 -0.064222404
## [778,] -0.2910828473 1.037401732
## [779,] 1.0576755474 1.082297288
## [780,] 0.0581371044 1.952565960
## [781,] 0.4177976085 -0.148491961
## [782,] 0.6460102264 0.437676087
## [783,] 0.2828478674 0.741023333
## [784,] 1.5485458561 0.126251536
## [785,] 1.4347841142 0.477089684
## [786,] 0.4988186903 -0.016685408
## [787,] 1.5534458648 0.104453367
## [788,] -0.0124976819 -0.101233586
## [789,] 0.3750115425 2.413935382
## [790,] 1.2343329280 0.792911281
## [791,] 0.1692699352 2.173926742
## [792,] 0.4028847038 0.975032968
## [793,] 0.2771378253 0.274963344
## [794,] 0.2325330317 0.765989246
## [795,] 0.3751012752 -0.112582884
## [796,] 0.3644075331 1.160137613
## [797,] 0.2698766066 2.329435006
## [798,] 2.6313364487 0.880469765
## [799,] 0.2095287430 5.851488052
## [800,] 0.2103946765 1.001996748
## [801,] 0.6412429751 0.481757617
## [802,] 1.9191357192 3.198050123
## [803,] 1.6685903222 1.511310418
## [804,] 0.3321925762 4.048305801
## [805,] 0.9424821586 0.051309446
## [806,] 0.1991158758 -0.004841911
## [807,] 0.6533141790 0.335737507
## [808,] 0.5087139095 0.489042616
## [809,] 0.0365241283 2.277848005
## [810,] 0.3257667716 0.844798877
## [811,] 1.1070927795 0.945504324
## [812,] -0.1345585190 2.716868296
## [813,] 1.8974424466 2.057999319
## [814,] 3.4785007311 0.636930095
## [815,] 2.2391541370 4.185997552
## [816,] 0.8267627368 -0.202842514
## [817,] -0.2299026529 0.266225155
## [818,] 0.5196673779 0.533826254
## [819,] 0.0336446681 0.022013846
## [820,] 0.2136450608 0.532539924
## [821,] 0.9969363697 1.830542300
## [822,] 0.5520974239 4.833531021
## [823,] 0.3226452971 0.894002559
## [824,] 1.2197786484 0.487676040
## [825,] -0.0166869627 0.962636077
## [826,] -0.1052274752 0.327606377
## [827,] 1.0600567342 0.914697653
## [828,] 0.6340094368 0.906339741
## [829,] 1.0244131442 0.376404757
## [830,] 2.8803966277 0.780930743
## [831,] -0.3186308990 3.925111984
## [832,] 0.8416583073 0.755608776
## [833,] 2.4485812085 2.149284085
## [834,] 1.0848855275 -0.184237519
## [835,] 0.1500888289 1.499025027
## [836,] 2.9740817902 1.438112273
## [837,] 1.9205032499 0.757579346
## [838,] 2.4328132874 0.809859732
## [839,] 0.3243629701 -0.157204953
## [840,] 2.9077321389 -0.236780288
## [841,] 2.9135548404 0.160355012
## [842,] 0.8143100977 1.641602943
## [843,] 0.1161796578 0.054754122
## [844,] -0.2576372201 4.687180445
## [845,] 0.2066499798 1.564421837
## [846,] 1.3737786598 0.455754853
## [847,] 1.8602754564 -0.278441477
## [848,] 1.1877224807 1.109120983
## [849,] -0.2749925926 1.289861374
## [850,] 2.1719869283 0.548049378
## [851,] 0.0311335158 1.443001242
## [852,] 1.9190048991 0.671241392
## [853,] 1.9760375263 1.211025935
## [854,] 3.0200189530 0.552194209
## [855,] 0.3194231054 0.998936993
## [856,] 0.3125369766 2.693485677
## [857,] 3.1957675726 0.852063744
## [858,] 2.3000843336 1.324582751
## [859,] 0.4818089629 0.778767051
## [860,] 0.8798237872 3.005648679
## [861,] 0.2263098937 2.471589619
## [862,] 0.2417144302 0.902273447
## [863,] -0.0655476363 1.218584357
## [864,] 1.7847285531 -0.159665149
## [865,] 0.0471599045 0.163122154
## [866,] 0.8596552747 0.298571672
## [867,] 2.1242133466 1.330526594
## [868,] 1.4823227974 -0.190681537
## [869,] 0.2628252893 3.041132280
## [870,] 0.6241788241 -0.140304429
## [871,] -0.2523421002 0.640761497
## [872,] 1.8667193724 0.767550895
## [873,] 1.9827944130 2.351128118
## [874,] 0.4364427348 0.753138551
## [875,] -0.0778379440 0.186609268
## [876,] 2.0169381685 0.232212526
## [877,] -0.2628504833 0.051916295
## [878,] 0.4145746778 0.833550167
## [879,] 0.8053700305 0.052815295
## [880,] 0.2572838033 1.376850972
## [881,] 1.1760205628 -0.094355383
## [882,] 1.4066968386 1.123415286
## [883,] -0.1288515725 3.122912318
## [884,] 0.7122703780 0.132387361
## [885,] 3.8848092018 0.947768223
## [886,] 0.6540975277 0.520864340
## [887,] 2.6205380493 0.797612800
## [888,] 0.7042258727 1.159796099
## [889,] 0.3957904116 0.303155405
## [890,] 0.0636852388 -0.171417291
## [891,] 2.0669337918 -0.146858850
## [892,] 0.1102113676 -0.250560128
## [893,] 2.8215446375 0.765378419
## [894,] 0.7266353723 2.704084956
## [895,] 2.0236857016 0.406396366
## [896,] 0.5767084678 1.415193371
## [897,] 0.6030039948 -0.001908206
## [898,] 5.2838148471 3.451443678
## [899,] 0.0729013262 0.669796735
## [900,] 0.8213443761 0.188597470
## [901,] -0.3262932995 1.215918777
## [902,] 0.2870888580 0.243571525
## [903,] 1.8591853313 2.453750808
## [904,] 1.8963306703 1.154836301
## [905,] 0.0380042131 1.281540592
## [906,] 0.4196498163 0.324187772
## [907,] 0.9731615797 2.100239419
## [908,] 1.0328296922 0.257511980
## [909,] 2.9663071236 0.616500731
## [910,] 0.5546955749 1.822032849
## [911,] -0.0553016672 0.583984812
## [912,] 1.3800093969 0.187232216
## [913,] 1.6813360977 0.457989461
## [914,] 2.3471992698 1.259808964
## [915,] 0.3218683947 2.855545999
## [916,] 2.7118654680 0.813195636
## [917,] 2.3585106863 1.152102697
## [918,] 2.5907293820 2.371054248
## [919,] 0.3036050624 1.319933730
## [920,] 0.4271359537 0.002995628
## [921,] 0.4242711659 2.026414320
## [922,] 1.0654576321 0.679857611
## [923,] 1.7711905985 0.516563779
## [924,] 0.5978366311 4.503201670
## [925,] 0.5191338120 0.974215747
## [926,] 13.3916782943 -0.079187511
## [927,] 2.9206745329 -0.035192100
## [928,] 2.5618845735 0.284313156
## [929,] 0.2977123530 0.193009727
## [930,] 2.9839684256 2.160921187
## [931,] 0.7364351556 2.722220100
## [932,] 0.4060467393 1.552907806
## [933,] 1.5803861103 2.541710870
## [934,] 0.3875621723 3.039902234
## [935,] 0.6731454887 3.323428626
## [936,] 2.7873234515 1.983347882
## [937,] 0.4102602934 2.385422829
## [938,] -0.1181172916 0.324337843
## [939,] 3.3509704004 -0.096167668
## [940,] 0.3691980839 0.309466038
## [941,] 0.3598752621 0.749909920
## [942,] 1.5123128923 0.171029016
## [943,] 0.0091500736 1.272607849
## [944,] -0.0987595486 0.626654926
## [945,] 0.1216604021 1.066659906
## [946,] 0.9477447074 0.527163579
## [947,] 1.4575926081 1.084193509
## [948,] 0.5559553770 0.242472788
## [949,] 2.1168721084 1.047687619
## [950,] 0.2569539473 0.829226054
## [951,] 0.3147368927 0.348493891
## [952,] 0.7808350229 -0.212736147
## [953,] 2.1461503389 -0.015433735
## [954,] 1.2817746538 0.122724743
## [955,] 0.2304499125 2.112342440
## [956,] 1.9670305671 1.339623969
## [957,] 1.1817900361 1.958412866
## [958,] 0.1913889326 0.936762027
## [959,] 0.4029531692 -0.202435530
## [960,] 0.7681921706 0.125969106
## [961,] 0.3067518914 0.548691189
## [962,] 0.4406931046 0.495322894
## [963,] 0.8782210114 0.382258716
## [964,] 0.2158108994 2.478298039
## [965,] 0.4455268900 0.792881219
## [966,] 0.3280753721 1.927580303
## [967,] 1.4996539213 -0.292105560
## [968,] 4.0182794385 4.905235287
## [969,] 0.3693551093 0.628189303
## [970,] 0.0297575121 1.784198139
## [971,] 0.9184103346 4.045041845
## [972,] 0.0899180282 1.191004808
## [973,] 0.8212161259 -0.081708949
## [974,] 3.6627517004 0.672799038
## [975,] 1.3322169209 0.612296017
## [976,] 0.0835673940 0.627223279
## [977,] 0.4391566444 0.210612498
## [978,] 1.3975443924 0.501346153
## [979,] 0.5200186388 1.097218328
## [980,] 0.3865665333 0.746815093
## [981,] 1.9374761507 1.179040504
## [982,] 1.4078087568 0.328575441
## [983,] 2.6983742808 2.547225766
## [984,] 0.8095081206 2.800580804
## [985,] 0.6640327044 0.118065791
## [986,] 0.5581794008 2.417895739
## [987,] 0.8641493265 3.037314825
## [988,] 0.2551565187 0.865663778
## [989,] -0.2104561120 0.376667709
## [990,] 2.9188440025 0.895658289
## [991,] -0.0137243973 0.323714279
## [992,] 0.5194672214 0.622105908
## [993,] 0.2304670501 -0.036684999
## [994,] 0.3006070909 0.646242590
## [995,] 3.0432435015 6.650730940
## [996,] 4.7231363582 2.352871762
## [997,] 3.5409136137 1.473905831
## [998,] 0.8601022134 1.875417900
## [999,] 0.0254516771 0.211900011
##
## $model.matrix
## (Intercept) avlength avcondition T_av O2_sat_av Con_av COD_av NH4._av
## 1 1 46.26316 0.7032430 11.114 81.286 740.7140 13.333 0.158
## 2 1 38.30000 0.6196317 10.440 65.400 609.6000 1.400 0.774
## 3 1 47.20000 0.7209293 12.858 75.333 434.7500 25.917 2.251
## 4 1 33.60000 0.7279046 13.086 72.857 949.0000 25.000 5.000
## 5 1 33.30769 0.6910252 11.750 96.833 896.1670 14.000 0.303
## 6 1 35.05263 0.7161573 13.557 84.571 340.7140 29.000 0.252
## 7 1 35.53333 0.7532778 11.750 88.417 716.3330 16.583 0.393
## 9 1 41.66667 0.8590551 12.008 87.000 800.3330 21.917 0.468
## 11 1 39.89474 0.6943120 13.550 63.167 527.6670 45.500 3.233
## 12 1 32.90909 0.6629259 14.957 65.857 1089.8570 50.500 5.365
## 13 1 39.88235 0.7324076 14.486 90.429 771.7143 11.333 0.668
## 14 1 38.42857 0.7578120 11.983 77.667 472.3330 37.167 2.367
## 15 1 34.23077 0.7642545 11.577 85.308 591.4620 24.667 0.260
## 16 1 43.61111 0.6500428 12.443 87.857 462.5710 37.167 0.210
## 17 1 40.16667 0.7486984 11.425 73.250 470.3330 20.000 2.589
## 18 1 37.43750 0.9549238 16.214 61.000 422.4290 35.833 2.417
## 19 1 41.47059 0.6295888 9.317 94.900 812.8330 7.500 0.103
## 20 1 28.00000 0.7382850 12.957 85.571 851.2860 15.833 0.170
## 21 1 37.84211 0.7477438 15.000 85.333 673.1670 19.500 0.635
## 22 1 42.00000 0.6865490 10.475 73.833 255.8330 24.727 0.527
## 23 1 36.50000 0.7425513 10.050 80.000 138.6670 37.167 0.290
## 24 1 43.58333 0.8243429 11.773 66.091 593.0910 34.727 0.806
## 26 1 31.72222 0.8809334 12.167 80.333 897.4170 16.417 0.409
## 28 1 40.31579 0.7644699 13.433 99.667 801.0000 20.909 0.354
## 29 1 39.25000 0.8383703 14.133 87.667 669.6670 14.000 0.227
## 30 1 42.37500 0.7907723 13.542 48.833 542.4170 42.333 2.261
## 31 1 42.00000 0.8148913 12.867 70.333 447.3330 29.333 0.680
## 32 1 38.87500 0.6351264 13.186 91.857 617.5710 19.333 0.362
## 33 1 37.31579 0.7512399 15.233 84.000 539.3330 13.667 0.325
## 34 1 38.73684 0.6217274 12.050 92.583 659.8330 14.750 0.336
## 35 1 37.85714 0.8194431 11.175 91.375 687.3750 26.000 0.395
## 36 1 32.88889 0.6862546 13.700 88.833 755.5000 26.167 1.260
## 38 1 34.65000 0.6616806 13.633 77.500 667.0000 20.333 0.695
## 39 1 33.25000 0.7554143 11.333 43.750 848.3330 35.750 2.542
## 40 1 36.75000 0.6487052 12.900 71.400 635.0000 16.400 2.208
## 41 1 36.35000 0.8265861 15.100 94.000 716.3330 15.167 0.165
## 42 1 35.15789 0.7600249 13.786 89.571 705.7140 18.167 0.880
## Nt_av pool_riffle1 meander1 netcen updist
## 1 8.917000 -1 -1 65212.97 67745.125
## 2 4.780000 1 1 50877.11 52437.119
## 3 8.925000 1 -1 38651.53 32574.449
## 4 9.067000 -1 -1 63911.70 65226.644
## 5 5.167000 1 -1 64168.17 67952.655
## 6 1.617000 1 1 45262.05 45780.074
## 7 2.775000 1 1 72386.11 76509.324
## 9 6.083000 1 -1 47724.46 49932.683
## 11 5.750000 1 1 49875.30 52217.733
## 12 16.100000 -1 -1 61880.37 26695.488
## 13 6.533000 1 1 60618.70 25511.682
## 14 7.000000 1 1 56056.62 15064.968
## 15 2.608000 -1 1 63687.75 67470.687
## 16 1.730000 -1 -1 68548.11 72561.660
## 17 10.617000 -1 -1 45271.82 39387.485
## 18 5.450000 1 -1 44142.92 15837.759
## 19 5.358361 -1 -1 64632.42 67396.486
## 20 4.583000 1 -1 72865.43 76898.411
## 21 5.067000 -1 1 58440.64 21751.460
## 22 2.164000 1 1 47879.02 44196.470
## 23 1.372000 1 1 53511.26 49989.625
## 24 4.891000 1 -1 37413.39 35027.425
## 26 5.242000 1 1 59347.24 62693.461
## 28 4.636000 1 -1 45740.16 46890.918
## 29 7.550000 -1 -1 73590.70 39137.994
## 30 3.317000 1 1 45131.08 29684.138
## 31 3.283000 1 1 43713.21 2368.891
## 32 4.767000 -1 -1 55885.32 18797.654
## 33 3.683000 1 1 63398.00 26850.462
## 34 8.050000 -1 1 65158.98 30465.362
## 35 4.317000 -1 -1 59901.23 62281.614
## 36 5.567000 -1 1 63856.37 66416.408
## 38 7.033000 1 1 53189.12 16286.394
## 39 5.017000 1 1 63663.04 23736.389
## 40 3.243000 1 1 60384.21 20784.664
## 41 2.550000 -1 -1 60481.19 20943.659
## 42 3.450000 1 1 64836.74 25423.656
##
## $terms
## meandist_bray ~ avlength + avcondition + T_av + O2_sat_av + Con_av +
## COD_av + NH4._av + Nt_av + pool_riffle + meander + netcen +
## updist
## attr(,"variables")
## list(meandist_bray, avlength, avcondition, T_av, O2_sat_av, Con_av,
## COD_av, NH4._av, Nt_av, pool_riffle, meander, netcen, updist)
## attr(,"factors")
## avlength avcondition T_av O2_sat_av Con_av COD_av NH4._av Nt_av
## meandist_bray 0 0 0 0 0 0 0 0
## avlength 1 0 0 0 0 0 0 0
## avcondition 0 1 0 0 0 0 0 0
## T_av 0 0 1 0 0 0 0 0
## O2_sat_av 0 0 0 1 0 0 0 0
## Con_av 0 0 0 0 1 0 0 0
## COD_av 0 0 0 0 0 1 0 0
## NH4._av 0 0 0 0 0 0 1 0
## Nt_av 0 0 0 0 0 0 0 1
## pool_riffle 0 0 0 0 0 0 0 0
## meander 0 0 0 0 0 0 0 0
## netcen 0 0 0 0 0 0 0 0
## updist 0 0 0 0 0 0 0 0
## pool_riffle meander netcen updist
## meandist_bray 0 0 0 0
## avlength 0 0 0 0
## avcondition 0 0 0 0
## T_av 0 0 0 0
## O2_sat_av 0 0 0 0
## Con_av 0 0 0 0
## COD_av 0 0 0 0
## NH4._av 0 0 0 0
## Nt_av 0 0 0 0
## pool_riffle 1 0 0 0
## meander 0 1 0 0
## netcen 0 0 1 0
## updist 0 0 0 1
## attr(,"term.labels")
## [1] "avlength" "avcondition" "T_av" "O2_sat_av" "Con_av"
## [6] "COD_av" "NH4._av" "Nt_av" "pool_riffle" "meander"
## [11] "netcen" "updist"
## attr(,"order")
## [1] 1 1 1 1 1 1 1 1 1 1 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"# environmental variables
env_select <- environment2[,c("T_av", "O2_sat_av", "Con_av", "COD_av", "NH4._av", "Nt_av", "pool_riffle", "meander", "netcen", "updist")]
env_select$pool_riffle <- as.numeric(env_select$pool_riffle)
env_select$meander <- as.numeric(env_select$meander)
pca <- prcomp(env_select, scale.=T)
summary(pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.7124 1.5545 1.1221 1.0140 0.88807 0.79463 0.56647
## Proportion of Variance 0.2933 0.2416 0.1259 0.1028 0.07887 0.06314 0.03209
## Cumulative Proportion 0.2933 0.5349 0.6608 0.7636 0.84248 0.90563 0.93771
## PC8 PC9 PC10
## Standard deviation 0.50483 0.46939 0.38429
## Proportion of Variance 0.02549 0.02203 0.01477
## Cumulative Proportion 0.96320 0.98523 1.00000plot(pca)
biplot(pca)
8.2.1 Effect of environment on infracommunity structure
# Assess the effect of environmental variables on parasite infracommunity dissimilarities using distance based RDA
spe.rda <- dbrda(meandist_bray ~ T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander, data = env_select)
anova(spe.rda)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = meandist_bray ~ T_av + O2_sat_av + Con_av + COD_av + NH4._av + Nt_av + pool_riffle + meander, data = env_select)
## Df SumOfSqs F Pr(>F)
## Model 8 0.45036 1.3833 0.01 **
## Residual 28 1.13946
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1RsquareAdj(spe.rda)$adj.r.squared## [1] 0.07850103mod0 <- dbrda(meandist_bray ~ 1, env_select) # Model with intercept only #edit_PH
mod1 <- dbrda(meandist_bray ~ ., env_select) # Model with all explanatory variables #edit_PH
step.res <- ordiR2step(mod0, mod1, direction = "both",perm.max = 200)## Step: R2.adj= 0
## Call: meandist_bray ~ 1
##
## R2.adjusted
## <All variables> 0.0898822750
## + Con_av 0.0492715387
## + NH4._av 0.0376899022
## + Nt_av 0.0353578268
## + COD_av 0.0097867139
## + updist 0.0092890919
## + pool_riffle 0.0070398050
## + netcen 0.0034499960
## + O2_sat_av 0.0031240320
## <none> 0.0000000000
## + T_av -0.0005031246
## + meander -0.0037253892
##
## Df AIC F Pr(>F)
## + Con_av 1 17.228 2.8657 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Step: R2.adj= 0.04927154
## Call: meandist_bray ~ Con_av
##
## R2.adjusted
## <All variables> 0.08988227
## + COD_av 0.08426096
## + NH4._av 0.07058808
## + updist 0.06800179
## + O2_sat_av 0.06266812
## + netcen 0.05455352
## + meander 0.05428744
## + Nt_av 0.05273614
## <none> 0.04927154
## + pool_riffle 0.04923534
## + T_av 0.04694754
##
## Df AIC F Pr(>F)
## + COD_av 1 16.768 2.3373 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Step: R2.adj= 0.08426096
## Call: meandist_bray ~ Con_av + COD_av
##
## R2.adjusted
## + updist 0.09961037
## + meander 0.09248673
## <All variables> 0.08988227
## + netcen 0.08626581
## + pool_riffle 0.08559576
## <none> 0.08426096
## + T_av 0.08115910
## + O2_sat_av 0.08092067
## + Nt_av 0.07979657
## + NH4._av 0.07591794step.res$anova # Summary table## R2.adj Df AIC F Pr(>F)
## + Con_av 0.049272 1 17.228 2.8657 0.002 **
## + COD_av 0.084261 1 16.768 2.3373 0.002 **
## <All variables> 0.089882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1spe.rda <- dbrda(meandist_bray ~ Con_av + COD_av, env_select)
plot(spe.rda, scaling = 1) # it is for technical reasons not possible to plot both site and species scores
summary(spe.rda)##
## Call:
## dbrda(formula = meandist_bray ~ Con_av + COD_av, data = env_select)
##
## Partitioning of squared Unknown distance:
## Inertia Proportion
## Total 1.5898 1.0000
## Constrained 0.2148 0.1351
## Unconstrained 1.3750 0.8649
##
## Eigenvalues, and their contribution to the squared Unknown distance
##
## Importance of components:
## dbRDA1 dbRDA2 MDS1 MDS2 MDS3 MDS4 MDS5
## Eigenvalue 0.1863 0.02858 0.1972 0.15873 0.1207 0.08661 0.07076
## Proportion Explained 0.1172 0.01798 0.1240 0.09984 0.0759 0.05447 0.04451
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS6 MDS7 MDS8 MDS9 MDS10 MDS11 MDS12
## Eigenvalue 0.06231 0.04802 0.04613 0.04387 0.04231 0.03826 0.03703
## Proportion Explained 0.03919 0.03021 0.02901 0.02760 0.02661 0.02406 0.02329
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS13 MDS14 MDS15 MDS16 MDS17 MDS18 MDS19
## Eigenvalue 0.03557 0.03355 0.03200 0.02808 0.02797 0.02575 0.02487
## Proportion Explained 0.02237 0.02110 0.02013 0.01766 0.01759 0.01620 0.01564
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS20 MDS21 MDS22 MDS23 MDS24 MDS25 MDS26
## Eigenvalue 0.02377 0.02238 0.02182 0.02104 0.01990 0.01880 0.01782
## Proportion Explained 0.01495 0.01408 0.01372 0.01323 0.01252 0.01183 0.01121
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS27 MDS28 MDS29 MDS30 MDS31 MDS32
## Eigenvalue 0.01719 0.01622 0.011778 0.010499 0.007903 0.006958
## Proportion Explained 0.01081 0.01020 0.007408 0.006604 0.004971 0.004376
## Cumulative Proportion NA NA NA NA NA NA
## MDS33 iMDS1
## Eigenvalue 0.005670 -0.006426
## Proportion Explained 0.003566 0.004042
## Cumulative Proportion NA NA
##
## Accumulated constrained eigenvalues
## Importance of components:
## dbRDA1 dbRDA2
## Eigenvalue 0.1863 0.02858
## Proportion Explained 0.8670 0.13304
## Cumulative Proportion 0.8670 1.00000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 2.750505
##
##
## Site scores (weighted sums of species scores)
##
## dbRDA1 dbRDA2 MDS1 MDS2 MDS3 MDS4
## SITE 1 -0.215744 -0.44381 0.01302 0.31819 -0.28116 -0.178171
## SITE 11 -0.507248 -0.82144 -0.09719 -0.28198 -0.04679 0.342258
## SITE 12 -0.746029 -0.15775 0.31672 0.25334 -0.08234 -0.151368
## SITE 13 -0.005847 0.21986 -0.02027 0.91847 -0.12233 -0.332031
## SITE 14 -0.101987 -0.28502 0.01314 0.55873 -0.09066 -0.223950
## SITE 15 -0.290401 0.51714 -0.23614 -0.10408 -0.36670 -0.263022
## SITE 16 0.016467 -0.22318 0.14693 -0.09879 -0.21010 0.156923
## SITE 17 -0.053594 -0.78979 0.68552 0.22623 0.55763 0.068555
## SITE 18 0.741297 0.34140 0.34156 -0.78781 -0.33039 0.372025
## SITE 19 2.204147 -0.31903 0.07576 -0.77176 -0.32619 0.237586
## SITE 2 -0.334822 -0.53901 0.20181 0.23760 0.25255 0.460075
## SITE 20 0.132501 1.43824 -0.36307 -0.15683 0.91526 0.652586
## SITE 21 -0.518164 -0.15765 0.51672 0.22690 -0.05180 0.071838
## SITE 22 -0.681351 0.71637 0.44536 0.61813 0.24285 -0.440713
## SITE 23 0.085803 0.07384 -0.18742 -0.58764 -0.50280 0.146049
## SITE 24 -0.506368 1.41199 -0.02580 0.53865 0.25884 0.040947
## SITE 26 0.017028 -0.61108 0.37526 -0.36715 -0.10492 0.425754
## SITE 28 0.121498 -0.47609 0.59944 -0.22080 -0.07107 0.465237
## SITE 29 -0.014961 -0.35365 0.04914 -0.10076 -0.08378 0.641604
## SITE 3 -0.711132 0.09645 0.08469 -0.08718 -0.21517 0.038217
## SITE 30 -0.812113 0.12175 0.55582 -0.16918 -0.25866 0.087617
## SITE 31 0.043616 0.61571 -0.31459 0.47663 -0.16031 -0.651535
## SITE 32 0.562390 -0.35603 -0.38737 0.06837 -0.20621 -0.072343
## SITE 33 -0.372173 -0.03501 0.10319 0.85337 -0.01445 -0.328696
## SITE 34 -0.466651 -0.48378 0.50738 0.02349 -0.08407 0.306511
## SITE 35 0.163917 1.66724 -0.04520 0.22652 0.57157 0.198776
## SITE 36 -0.296043 0.76361 -0.59812 0.40106 -0.04152 -0.249681
## SITE 38 0.302702 -0.34509 -0.16274 -0.49724 -0.25040 -0.005416
## SITE 39 -0.978171 -0.31142 0.36167 0.40699 0.02355 0.020399
## SITE 4 -0.141634 -0.44343 -0.02774 -0.11080 -0.07834 0.180628
## SITE 40 0.496377 0.01681 -0.38350 -0.04028 -0.50240 -0.167352
## SITE 41 1.117637 -0.21808 -0.82799 -0.60061 -0.49878 -0.637014
## SITE 42 0.437517 0.97421 -1.46550 0.08641 1.33505 0.880493
## SITE 5 0.522987 -0.17736 0.27513 0.18757 -0.04206 0.152978
## SITE 6 0.423826 0.07352 -0.91744 -0.10155 -0.42928 -0.789691
## SITE 7 0.013170 -1.17661 0.57227 -1.15454 1.52741 -1.616432
## SITE 9 0.351552 -0.32386 -0.18044 -0.38769 -0.23204 0.160360
##
##
## Site constraints (linear combinations of constraining variables)
##
## dbRDA1 dbRDA2 MDS1 MDS2 MDS3 MDS4
## SITE 1 -0.079483 -0.44275 0.01302 0.31819 -0.28116 -0.178171
## SITE 11 -0.692558 -0.69501 -0.09719 -0.28198 -0.04679 0.342258
## SITE 12 -0.387516 0.29270 0.31672 0.25334 -0.08234 -0.151368
## SITE 13 0.692033 -0.28074 -0.02027 0.91847 -0.12233 -0.332031
## SITE 14 0.273072 -0.58517 0.01314 0.55873 -0.09066 -0.223950
## SITE 15 -0.503973 0.49304 -0.23614 -0.10408 -0.36670 -0.263022
## SITE 16 -0.041630 -0.31054 0.14693 -0.09879 -0.21010 0.156923
## SITE 17 0.286975 -0.22462 0.68552 0.22623 0.55763 0.068555
## SITE 18 0.355899 0.83587 0.34156 -0.78781 -0.33039 0.372025
## SITE 19 1.702755 0.40556 0.07576 -0.77176 -0.32619 0.237586
## SITE 2 -0.068404 -0.54101 0.20181 0.23760 0.25255 0.460075
## SITE 20 0.005618 0.62143 -0.36307 -0.15683 0.91526 0.652586
## SITE 21 -0.085484 0.08616 0.51672 0.22690 -0.05180 0.071838
## SITE 22 -0.015359 0.63175 0.44536 0.61813 0.24285 -0.440713
## SITE 23 -0.475351 0.06131 -0.18742 -0.58764 -0.50280 0.146049
## SITE 24 -0.138656 0.63048 -0.02580 0.53865 0.25884 0.040947
## SITE 26 -0.086478 -0.70999 0.37526 -0.36715 -0.10492 0.425754
## SITE 28 0.227529 -0.47771 0.59944 -0.22080 -0.07107 0.465237
## SITE 29 -0.053389 -0.16940 0.04914 -0.10076 -0.08378 0.641604
## SITE 3 -0.805011 0.44279 0.08469 -0.08718 -0.21517 0.038217
## SITE 30 -0.711357 0.97402 0.55582 -0.16918 -0.25866 0.087617
## SITE 31 0.197349 0.41392 -0.31459 0.47663 -0.16031 -0.651535
## SITE 32 0.342870 -0.50733 -0.38737 0.06837 -0.20621 -0.072343
## SITE 33 0.260419 -0.25833 0.10319 0.85337 -0.01445 -0.328696
## SITE 34 -0.213627 -0.34583 0.50738 0.02349 -0.08407 0.306511
## SITE 35 0.299656 0.71657 -0.04520 0.22652 0.57157 0.198776
## SITE 36 -0.265626 0.39128 -0.59812 0.40106 -0.04152 -0.249681
## SITE 38 -0.177490 -0.11612 -0.16274 -0.49724 -0.25040 -0.005416
## SITE 39 -0.502932 -0.21901 0.36167 0.40699 0.02355 0.020399
## SITE 4 -0.213933 -0.31087 -0.02774 -0.11080 -0.07834 0.180628
## SITE 40 0.157625 0.02847 -0.38350 -0.04028 -0.50240 -0.167352
## SITE 41 0.308647 -0.03805 -0.82799 -0.60061 -0.49878 -0.637014
## SITE 42 -0.043511 -0.13560 -1.46550 0.08641 1.33505 0.880493
## SITE 5 0.774214 0.17770 0.27513 0.18757 -0.04206 0.152978
## SITE 6 -0.221478 -0.23059 -0.91744 -0.10155 -0.42928 -0.789691
## SITE 7 -0.080948 -0.35692 0.57227 -1.15454 1.52741 -1.616432
## SITE 9 -0.020466 -0.24745 -0.18044 -0.38769 -0.23204 0.160360
##
##
## Biplot scores for constraining variables
##
## dbRDA1 dbRDA2 MDS1 MDS2 MDS3 MDS4
## Con_av 0.7628 -0.6467 0 0 0 0
## COD_av 0.4413 0.8974 0 0 0 0anova(spe.rda)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = meandist_bray ~ Con_av + COD_av, data = env_select)
## Df SumOfSqs F Pr(>F)
## Model 2 0.21484 2.6563 0.001 ***
## Residual 34 1.37498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(spe.rda, by="term")## Permutation test for dbrda under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = meandist_bray ~ Con_av + COD_av, data = env_select)
## Df SumOfSqs F Pr(>F)
## Con_av 1 0.12032 2.9752 0.002 **
## COD_av 1 0.09452 2.3373 0.009 **
## Residual 34 1.37498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 18.2.2 Effect of space on infracommunity structure
spe.rda <- dbrda(meandist_bray ~ netcen + updist, data = env_select)
anova(spe.rda)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = meandist_bray ~ netcen + updist, data = env_select)
## Df SumOfSqs F Pr(>F)
## Model 2 0.10742 1.2319 0.163
## Residual 34 1.48240RsquareAdj(spe.rda)$adj.r.squared## [1] 0.01271734anova.cca(spe.rda, step=1000);## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = meandist_bray ~ netcen + updist, data = env_select)
## Df SumOfSqs F Pr(>F)
## Model 2 0.10742 1.2319 0.138
## Residual 34 1.48240anova.cca(spe.rda, step=1000, by="term");## Permutation test for dbrda under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = meandist_bray ~ netcen + updist, data = env_select)
## Df SumOfSqs F Pr(>F)
## netcen 1 0.04949 1.1352 0.267
## updist 1 0.05792 1.3285 0.125
## Residual 34 1.48240RsquareAdj(spe.rda)$adj.r.squared;## [1] 0.01271734RsquareAdj(spe.rda)$r.squared## [1] 0.067566378.2.3 Variation partitioning
#Variation partitioning
spe.varpart1 <- varpart(meandist_bray, env_select[,1:8], env_select[,9:10])
plot(spe.varpart1,digits=2)
spe.varpart1##
## Partition of squared Unknown user-supplied distance in dbRDA
##
## Call: varpart(Y = meandist_bray, X = env_select[, 1:8], env_select[,
## 9:10])
##
## Explanatory tables:
## X1: env_select[, 1:8]
## X2: env_select[, 9:10]
##
## No. of explanatory tables: 2
## Total variation (SS): 1.5898
## No. of observations: 37
##
## Partition table:
## Df R.squared Adj.R.squared Testable
## [a+c] = X1 8 0.28328 0.07850 TRUE
## [b+c] = X2 2 0.06757 0.01272 TRUE
## [a+b+c] = X1+X2 10 0.34269 0.08988 TRUE
## Individual fractions
## [a] = X1|X2 8 0.07716 TRUE
## [b] = X2|X1 2 0.01138 TRUE
## [c] 0 0.00134 FALSE
## [d] = Residuals 0.91012 FALSE
## ---
## Use function 'dbrda' to test significance of fractions of interestanova.cca(dbrda(meandist_bray ~ T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander + Condition(netcen + updist),
data=env_select), step=1000)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = meandist_bray ~ T_av + O2_sat_av + Con_av + COD_av + NH4._av + Nt_av + pool_riffle + meander + Condition(netcen + updist), data = env_select)
## Df SumOfSqs F Pr(>F)
## Model 8 0.4374 1.3603 0.015 *
## Residual 26 1.0450
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova.cca(dbrda(meandist_bray ~ netcen + updist+
Condition(T_av + O2_sat_av + Con_av + COD_av
+ NH4._av + Nt_av + pool_riffle + meander), data=env_select), step=1000)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = meandist_bray ~ netcen + updist + Condition(T_av + O2_sat_av + Con_av + COD_av + NH4._av + Nt_av + pool_riffle + meander), data = env_select)
## Df SumOfSqs F Pr(>F)
## Model 2 0.09446 1.1751 0.172
## Residual 26 1.04500